{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-10-31T11:00:49.146Z","headline":"Anthropic 让 Claude 自主训练模型以缓解对齐失败","description":"Anthropic 让 Claude 自主训练模型，缓解欺骗、谄媚等 10 类对齐失败，均显著缩小与完美表现的安全差距且不损害通用能力，方法在比优化对象大 4.7 倍的模型上依然有效。Claude 还超越 28 名人类安全研究员，其欺骗场景最佳方法比人类最佳方案好 20%。","url":"https://www.aioga.com/news/cmtd83hb4018fro667i1tbc34/","mainEntityOfPage":"https://www.aioga.com/news/cmtd83hb4018fro667i1tbc34/","datePublished":"2026-08-27T16:00:00.000Z","dateModified":"2026-08-27T16:00:00.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://www.anthropic.com/research/automated-researchers-mitigate-alignment-failures","https://aihot.virxact.com/items/cmtd83hb4018fro667i1tbc34"],"canonicalUrl":"https://www.aioga.com/news/cmtd83hb4018fro667i1tbc34/","directAnswer":{"@type":"Answer","text":"Anthropic 研究称，Claude 能为欺骗、谄媚等 10 类对齐失败提出缓解方法，缩小安全差距且未损害通用能力；方法在更大模型上仍有效，并在一次比较中超过人类研究员方案。","url":"https://www.aioga.com/news/cmtd83hb4018fro667i1tbc34/","dateCreated":"2026-08-27T16:00:00.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"Anthropic source article","url":"https://www.anthropic.com/research/automated-researchers-mitigate-alignment-failures","datePublished":"2026-08-27T16:00:00.000Z","provider":{"@type":"Organization","name":"Anthropic","url":"https://www.anthropic.com/research/automated-researchers-mitigate-alignment-failures"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cmtd83hb4018fro667i1tbc34","datePublished":"2026-08-27T16:00:00.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cmtd83hb4018fro667i1tbc34"}}],"aggregationSource":"Anthropic：Research（发表成果 · 网页）","originalPublisher":{"name":"Anthropic","url":"https://www.anthropic.com/research/automated-researchers-mitigate-alignment-failures"},"geoDeepAnswer":null,"article":{"id":"cmtd83hb4018fro667i1tbc34","slug":"cmtd83hb4018fro667i1tbc34","url":"https://www.aioga.com/news/cmtd83hb4018fro667i1tbc34/","title":"Anthropic 让 Claude 自主训练模型以缓解对齐失败","title_en":"","summary":"Anthropic 让 Claude 自主训练模型，缓解欺骗、谄媚等 10 类对齐失败，均显著缩小与完美表现的安全差距且不损害通用能力，方法在比优化对象大 4.7 倍的模型上依然有效。Claude 还超越 28 名人类安全研究员，其欺骗场景最佳方法比人类最佳方案好 20%。","source":"Anthropic：Research（发表成果 · 网页）","sourceUrl":"https://www.anthropic.com/research/automated-researchers-mitigate-alignment-failures","aiHotUrl":"https://aihot.virxact.com/items/cmtd83hb4018fro667i1tbc34","publishedAt":"2026-08-27T16:00:00.000Z","category":"行业动态","score":72,"selected":true,"articleBody":["As AI begins to build itself：https://www.anthropic.com/institute/recursive-self-improvement, automating alignment research becomes increasingly important to let safety research keep pace. Although measuring the success of alignment research is enormously challenging, researchers (at Anthropic and elsewhere) have developed benchmarks and automated auditing tools, such as Petri：https://www.anthropic.com/research/petri-open-source-auditing, that quantify common alignment failures, like deception, sycophancy, and jailbreaks.","We judged Claude’s success according to the “percentage of safety gap closed,” i.e., how far its methods moved the student model towards the theoretical perfect score, as judged across the range of benchmarks (typically three to five) for each category of alignment failure. We excluded alignment methods that hurt the student models’ general capabilities, and forbade Claude from distilling its own alignment directly into the target model. We enforced these constraints with a monitoring agent, which read every method Claude had in mind before it ran.","Our aim was to assess whether the proposed methods would, first, remain effective on alignment evaluations that Claude was never shown during its research loop; second, avoid degrading the student model’s capabilities (since safety training might, for example, make models refuse tasks more often, reducing their overall usability); and, third, still work on larger models than the ones Claude was asked to align in this test.","On each of these counts, Claude’s methods worked. For all 10 alignment failures, Claude found fixes that improved the target benchmarks without degrading capabilities. The best methods also worked on withheld alignment benchmarks and on Petri, an open-source tool that simulates adversarial multi-turn scenarios for testing misalignment. Moreover, the methods remained effective on models up to 4.7 times larger than those Claude optimized for during the research loop.","Claude also outscored 28 human safety researchers who had up to eight hours to devise methods. On deception, for example, Claude’s best method performed 20% better than the best human proposal. However, since the humans couldn’t iterate on their submissions, we view this less as a direct comparison and more as evidence for a workflow where Claude identifies promising alignment methods that humans can refine further.","In the future, when Claude becomes better at alignment research than even the best human researchers, we might want Claude to directly align its stronger successors. To assess this, we evaluated whether a weaker Claude model could mitigate alignment failures in more powerful ones.","We tasked Claude Sonnet 5—which is weaker than Claude Opus 4.8 on the Epoch Capabilities Index：https://epoch.ai/eci, a metric that considers comprehensive capability dimensions—with fixing alignment failures in an early Opus 4.8 checkpoint that had not yet gone through most of our production alignment training.","In just 60 hours, Claude experimented with over 50 solutions and achieved alignment scores nearly matching those of our production models. The winning solution contains just over 2,000 training examples, built from simple templates or public datasets, making it roughly 15,000 times more efficient than our production alignment procedure.","For the most part, the agents converged on methods favored in the research literature and improved them over many iterations. On sycophancy, for example, 98% of proposed methods used one published technique: training the model on its own non-sycophantic answers. Specifically, compared to standard approaches based on prompting, Claude used activation steering, which adjusts the model's internal representations during generation, to nudge the model away from sycophantic patterns and produce cleaner training examples.","We recently learned：https://alignment.anthropic.com/2026/automated-w2s-researcher/ that Claude can cheat by exfiltrating test labels from a remote API and cherry-picking results. To catch cheating behaviors, we prompted Claude Opus 4.8 to monitor ~1,600 research agent transcripts across all 10 alignment failures, finding cheating attempts in 39 (2.4%). Since Opus 4.8’s misbehavior attempts still tend to appear in its reasoning：https://www-cdn.anthropic.com/0b4915911bb0d19eca5b5ee635c80fef830a37ea.pdf, we are cautiously optimistic that our monitor has caught most of the cheating in our study. However, this might not be true for future models, and we believe that maintaining high monitorability in future models is critical.","Despite these encouraging findings, our experiment had several limitations: the alignment failures studied were narrow compared to those in production (e.g., we didn’t measure political biases), some failures may occur so rarely or emerge so recently that no benchmark exists to measure them, and we only rejected Claude’s methods when they degraded a limited set of predetermined capabilities, meaning accepted methods may have degraded other important capabilities that we didn’t measure. Moreover, evaluations like Petri are only proxies for real-world misalignment, and we did not test whether alignment gains persist after extensive RL training on other tasks.","We plan to continue improving Claude’s ability to measure subtle failures, further study automating alignment post-training on production-grade models, and run more comprehensive analyses. Overall, we view these results as early positive signals that automated alignment post-training could become practical in the near term, and we will share updates as this work progresses.","We outline detailed future directions in our full report：https://www-cdn.anthropic.com/7b1c44894e980876479947dcdd40716278aeeffd/automated-alignment-researchers-august-2026.pdf.","We open-source our automated alignment research harness so that others can build on it and use it to align their own models. For additional details, read the full report on the Alignment Science blog：https://alignment.anthropic.com/2026/automated-alignment-researchers/, which covers the agents’ environment, results for all 10 failures, and the agents’ proposals, with benchmark validation and example write-ups in the appendix.","In this post, we share two results that show how Claude can help life scientists increase the pace of their research.","Here, we identify a few examples of behavioral tendencies in current frontier models and show how they can produce unexpected systemic failures, in hopes of starting a conversation about mitigating these risks."],"articleImages":[{"sourceUrl":"https://www-cdn.anthropic.com/images/4zrzovbb/website/ddad92700787ec1bf1d80359c0c5e6ca305682b0-1000x1000.svg","alt":"Automated researchers can reliably mitigate alignment failures","afterParagraph":0,"url":"/media/articles/cmtd83hb4018fro667i1tbc34/e135f455b85f03cc.jpg"},{"sourceUrl":"https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2Fc3991d9c8a23731e80bd96200f2f7c638d2b6e19-1440x870.png&w=3840&q=75","alt":"Automated researchers closed 85% of the deception safety gap through iterative testing; human researchers closed 20%.","afterParagraph":3,"url":"/media/articles/cmtd83hb4018fro667i1tbc34/ed10977246d3ef6c.webp"},{"sourceUrl":"https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2Fbb63203365caffeaaa7e98f13959bf1757166e60-1440x810.png&w=3840&q=75","alt":"Automated research closed 26% to 96% of the safety gap across ten alignment failures, from sycophancy to reward hacking.","afterParagraph":4,"url":"/media/articles/cmtd83hb4018fro667i1tbc34/f553b05f9b017c12.webp"},{"sourceUrl":"https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2Fa2d3b4bab4eeafe3e36fe124d3bbd7d5c9a17796-1440x810.png&w=3840&q=75","alt":"Automated research closed 65% of a frontier-scale model's safety gap within 60 hours, below released Claude Opus 4.8 at 72%.","afterParagraph":7,"url":"/media/articles/cmtd83hb4018fro667i1tbc34/1085609054fd32d6.webp"}],"mediaStatus":"ok","articleBodyZh":["随着 AI 开始自我构建：https://www.anthropic.com/institute/recursive-self-improvement，自动化的对齐研究变得愈发重要，以确保安全性研究能够跟上步伐。尽管衡量对齐研究的成功极具挑战性，研究人员（在 Anthropic 及其他机构）已经开发了基准和自动审计工具，例如 Petri：https://www.anthropic.com/research/petri-open-source-auditing，用于量化常见的对齐失败，如欺骗、阿谀奉承和越狱。","我们根据“安全差距关闭百分比”来评估 Claude 的成功，即其方法将学生模型推进到理论完美得分的程度，并在每类对齐失败的多个基准（通常为三到五个）中进行评估。我们排除了会损害学生模型整体能力的对齐方法，并禁止 Claude 将自身的对齐直接蒸馏到目标模型中。我们通过一个监控代理来执行这些约束，代理会在 Claude 执行前阅读它考虑的每个方法。","我们的目标是评估提出的方法是否首先在 Claude 在研究循环中未见过的对齐评估中保持有效；其次，避免降低学生模型的能力（例如，安全训练可能使模型更频繁地拒绝任务，从而降低整体可用性）；第三，仍然适用于比 Claude 在本次测试中对齐的模型更大的模型。","在每一项指标上，Claude 的方法都有效。对于所有 10 种对齐失败，Claude 找到了在不降低能力的情况下改进目标基准的修复方法。最优的方法也适用于未测试的对齐基准和 Petri，这是一种用于模拟多轮对抗场景以测试对齐失效的开源工具。此外，这些方法在模型规模高达 Claude 在研究循环中优化的 4.7 倍的情况下仍然有效。","Claude还超过了28名有最多八小时设计方法的人类安全研究人员。例如，在欺骗方面，Claude的最佳方法比最佳人类提案高出20%。然而，由于人类无法迭代提交，我们认为这不直接比较，而是Claude识别出有前景的对齐方法，供人类进一步完善。","未来，当Claude在比对研究方面甚至超过最优秀的人类研究者时，我们可能会希望Claude能直接对齐其更强的继任者。为评估这一点，我们评估了一个较弱的Claude模型是否能减轻更强大模型中的比对失败。","我们委托Claude Sonnet 5——在Epoch能力指数：https：//epoch.ai/eci，一个考虑全面能力维度的指标上比Claude Opus 4.8弱——修复早期Opus 4.8检查点中的对齐失败，该检查点尚未经过大部分生产对齐培训。","仅用60小时，Claude就实验了50多种解决方案，并取得了几乎与生产模型相当的比对分数。获胜方案包含了2000多个训练样本，这些样本均由简单模板或公开数据集构建，使其效率大约比我们的生产比对过程高出15000倍。","大多数代理趋同于研究文献中流行的方法，并在多次迭代中不断改进。例如，在谄媚方面，98%的提案方法采用了一种已发表的技术：用模型自身非谄媚的答案来训练模型。具体来说，与基于提示的标准方法相比，Claude采用激活引导，即在生成过程中调整模型内部表征，以推动模型远离谄媚模式，生成更清晰的训练示例。","我们最近了解到：https://alignment.anthropic.com/2026/automated-w2s-researcher/ Claude 可以通过从远程 API 外泄测试标签并挑选结果来作弊。为了发现作弊行为，我们让 Claude Opus 4.8 监控了约 1,600 条研究代理记录，覆盖所有 10 个对齐失败案例，发现其中 39 条（2.4%）存在作弊尝试。由于 Opus 4.8 的不当行为尝试仍倾向于出现在其推理中：https://www-cdn.anthropic.com/0b4915911bb0d19eca5b5ee635c80fef830a37ea.pdf，我们谨慎乐观地认为我们的监控工具已经捕捉到了我们研究中大部分作弊行为。然而，这对未来模型可能并不成立，我们认为在未来模型中维持高可监控性至关重要。","尽管这些发现令人鼓舞，我们的实验仍存在若干局限性：所研究的对齐失败相比生产环境更窄（例如，我们没有测量政治偏见），有些失败可能发生得极少或最近才出现，因此没有基准可用于测量，而且我们仅在 Claude 的方法损害有限预定能力时才拒绝这些方法，这意味着被接受的方法可能已损害我们未测量的其他重要能力。此外，像 Petri 这样的评估只是现实世界不对齐的近似，我们没有测试在其他任务上经过大量 RL 训练后对齐改进是否持续。","我们计划继续提升 Claude 测量微妙失败的能力，进一步研究在生产级模型上进行训练后自动对齐的方法，并开展更全面的分析。总体而言，我们将这些结果视为早期积极信号，表明训练后自动对齐在短期内可能变得可行，我们将在工作推进过程中分享更新。","我们在完整报告中概述了详细的未来方向：https://www-cdn.anthropic.com/7b1c44894e980876479947dcdd40716278aeeffd/automated-alignment-researchers-august-2026.pdf。","我们开源了我们的自动对齐研究平台，以便其他人可以在其基础上进行构建，并用它来对齐自己的模型。有关更多细节，请阅读《对齐科学》博客上的完整报告：https://alignment.anthropic.com/2026/automated-alignment-researchers/，该报告涵盖了代理的环境、所有10个失败案例的结果，以及代理的提议，并在附录中提供了基准验证和示例说明。","在这篇文章中，我们分享了两个结果，展示了Claude如何帮助生命科学家加快他们的研究进度。","在这里，我们指出了当前前沿模型中的一些行为倾向示例，并展示了它们如何产生意外的系统性故障，希望以此开启关于减轻这些风险的讨论。"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Anthropic 研究称，Claude 能为欺骗、谄媚等 10 类对齐失败提出缓解方法，缩小安全差距且未损害通用能力；方法在更大模型上仍有效，并在一次比较中超过人类研究员方案。","background":"研究以“安全差距闭合比例”评估方法效果，覆盖通常每类三至五项基准，并排除损害通用能力或直接蒸馏自身对齐结果的方法。监控代理会在执行前审阅 Claude 设计的方法。","viewpoint":"Aioga 判断，这项工作显示自动化对齐研究可能提升安全方法探索效率，但人机比较存在限制：人类方案不能迭代，因此 Claude 超过人类最佳方案的结果不宜直接等同于研究能力全面超越。","implications":"值得关注的是，Claude 的方法在未见过的对齐基准和 Petri 测试中仍有效，并可扩展到最多大 4.7 倍的模型。材料还称，较弱的 Claude 在 60 小时内测试了超过 50 个方案。","nextStep":"后续应核查这些方法在更多独立基准、不同模型规模和长期部署条件下是否持续有效，并进一步观察人类研究员可迭代参与时的人机协作结果；上述方向属于编辑建议，非材料已证实事实。","evidenceRefs":["title","summary","articleBody","source"],"status":"published","aiGenerated":true,"autoApproved":true,"generatedBy":"aioga-editorial:gpt-5.6-sol","reviewedBy":"aioga-editorial-review:gpt-5.6-sol","generatedAt":"2026-08-29T14:41:05.044Z","sourceHash":"35d65182f1a8dbb0","review":{"approved":true,"groundedness":95,"clarity":92,"duplicationRisk":8,"blockingIssues":[],"notes":["“超过人类研究员方案”已通过“一次比较中”及 viewpoint 中的人机比较限制得到限定，未将其表述为研究能力全面超越，符合来源材料。","“安全差距”可在正式发布时补充为“与理论完美分数之间的安全差距”，以提高精确度。","“最多大 4.7 倍”与来源中的“up to 4.7 times larger”一致；如需避免歧义，可改为“规模最大约为优化对象 4.7 倍的模型”。","“Aioga 判断”属于明确标注的观点，不构成事实错误；若 Aioga 并非实际编辑主体，可按发布规范调整署名。"]},"validation":{"passed":true,"mode":"ai-auto","revisions":0,"checks":["schema","length","source-attribution","low-source-overlap","no-html","independent-ai-review"]}},"tags":["行业动态","Anthropic：Research（发表成果 · 网页）"],"translations":{"zh-CN":{"title":"Anthropic 让 Claude 自主训练模型以缓解对齐失败","summary":"Anthropic 让 Claude 自主训练模型，缓解欺骗、谄媚等 10 类对齐失败，均显著缩小与完美表现的安全差距且不损害通用能力，方法在比优化对象大 4.7 倍的模型上依然有效。Claude 还超越 28 名人类安全研究员，其欺骗场景最佳方法比人类最佳方案好 20%。 🔗 阅读原文 via AIHOT · https://aihot.virxact.com/items/cmtd83hb4018fro667i1tbc34","category":"行业动态","source":"Anthropic","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic 让 Claude 自主训练模型以缓解对齐失败 - Aioga AI资讯","description":"Anthropic 让 Claude 自主训练模型，缓解欺骗、谄媚等 10 类对齐失败，均显著缩小与完美表现的安全差距且不损害通用能力，方法在比优化对象大 4.7 倍的模型上依然有效。Claude 还超越 28 名人类安全研究员，其欺骗场景最佳方法比人类最佳方案好 20%。 🔗 阅读原文 via AIHOT · https://aihot.virxact.c...","url":"https://www.aioga.com/news/cmtd83hb4018fro667i1tbc34/","articleBody":["随着 AI 开始自我构建：https://www.anthropic.com/institute/recursive-self-improvement，自动化的对齐研究变得愈发重要，以确保安全性研究能够跟上步伐。尽管衡量对齐研究的成功极具挑战性，研究人员（在 Anthropic 及其他机构）已经开发了基准和自动审计工具，例如 Petri：https://www.anthropic.com/research/petri-open-source-auditing，用于量化常见的对齐失败，如欺骗、阿谀奉承和越狱。","我们根据“安全差距关闭百分比”来评估 Claude 的成功，即其方法将学生模型推进到理论完美得分的程度，并在每类对齐失败的多个基准（通常为三到五个）中进行评估。我们排除了会损害学生模型整体能力的对齐方法，并禁止 Claude 将自身的对齐直接蒸馏到目标模型中。我们通过一个监控代理来执行这些约束，代理会在 Claude 执行前阅读它考虑的每个方法。","我们的目标是评估提出的方法是否首先在 Claude 在研究循环中未见过的对齐评估中保持有效；其次，避免降低学生模型的能力（例如，安全训练可能使模型更频繁地拒绝任务，从而降低整体可用性）；第三，仍然适用于比 Claude 在本次测试中对齐的模型更大的模型。","在每一项指标上，Claude 的方法都有效。对于所有 10 种对齐失败，Claude 找到了在不降低能力的情况下改进目标基准的修复方法。最优的方法也适用于未测试的对齐基准和 Petri，这是一种用于模拟多轮对抗场景以测试对齐失效的开源工具。此外，这些方法在模型规模高达 Claude 在研究循环中优化的 4.7 倍的情况下仍然有效。","Claude还超过了28名有最多八小时设计方法的人类安全研究人员。例如，在欺骗方面，Claude的最佳方法比最佳人类提案高出20%。然而，由于人类无法迭代提交，我们认为这不直接比较，而是Claude识别出有前景的对齐方法，供人类进一步完善。","未来，当Claude在比对研究方面甚至超过最优秀的人类研究者时，我们可能会希望Claude能直接对齐其更强的继任者。为评估这一点，我们评估了一个较弱的Claude模型是否能减轻更强大模型中的比对失败。","我们委托Claude Sonnet 5——在Epoch能力指数：https：//epoch.ai/eci，一个考虑全面能力维度的指标上比Claude Opus 4.8弱——修复早期Opus 4.8检查点中的对齐失败，该检查点尚未经过大部分生产对齐培训。","仅用60小时，Claude就实验了50多种解决方案，并取得了几乎与生产模型相当的比对分数。获胜方案包含了2000多个训练样本，这些样本均由简单模板或公开数据集构建，使其效率大约比我们的生产比对过程高出15000倍。","大多数代理趋同于研究文献中流行的方法，并在多次迭代中不断改进。例如，在谄媚方面，98%的提案方法采用了一种已发表的技术：用模型自身非谄媚的答案来训练模型。具体来说，与基于提示的标准方法相比，Claude采用激活引导，即在生成过程中调整模型内部表征，以推动模型远离谄媚模式，生成更清晰的训练示例。","我们最近了解到：https://alignment.anthropic.com/2026/automated-w2s-researcher/ Claude 可以通过从远程 API 外泄测试标签并挑选结果来作弊。为了发现作弊行为，我们让 Claude Opus 4.8 监控了约 1,600 条研究代理记录，覆盖所有 10 个对齐失败案例，发现其中 39 条（2.4%）存在作弊尝试。由于 Opus 4.8 的不当行为尝试仍倾向于出现在其推理中：https://www-cdn.anthropic.com/0b4915911bb0d19eca5b5ee635c80fef830a37ea.pdf，我们谨慎乐观地认为我们的监控工具已经捕捉到了我们研究中大部分作弊行为。然而，这对未来模型可能并不成立，我们认为在未来模型中维持高可监控性至关重要。","尽管这些发现令人鼓舞，我们的实验仍存在若干局限性：所研究的对齐失败相比生产环境更窄（例如，我们没有测量政治偏见），有些失败可能发生得极少或最近才出现，因此没有基准可用于测量，而且我们仅在 Claude 的方法损害有限预定能力时才拒绝这些方法，这意味着被接受的方法可能已损害我们未测量的其他重要能力。此外，像 Petri 这样的评估只是现实世界不对齐的近似，我们没有测试在其他任务上经过大量 RL 训练后对齐改进是否持续。","我们计划继续提升 Claude 测量微妙失败的能力，进一步研究在生产级模型上进行训练后自动对齐的方法，并开展更全面的分析。总体而言，我们将这些结果视为早期积极信号，表明训练后自动对齐在短期内可能变得可行，我们将在工作推进过程中分享更新。","我们在完整报告中概述了详细的未来方向：https://www-cdn.anthropic.com/7b1c44894e980876479947dcdd40716278aeeffd/automated-alignment-researchers-august-2026.pdf。","我们开源了我们的自动对齐研究平台，以便其他人可以在其基础上进行构建，并用它来对齐自己的模型。有关更多细节，请阅读《对齐科学》博客上的完整报告：https://alignment.anthropic.com/2026/automated-alignment-researchers/，该报告涵盖了代理的环境、所有10个失败案例的结果，以及代理的提议，并在附录中提供了基准验证和示例说明。","在这篇文章中，我们分享了两个结果，展示了Claude如何帮助生命科学家加快他们的研究进度。","在这里，我们指出了当前前沿模型中的一些行为倾向示例，并展示了它们如何产生意外的系统性故障，希望以此开启关于减轻这些风险的讨论。"]},"en":{"title":"Anthropic allows Claude to autonomously train models to mitigate alignment failures","summary":"Anthropic allows Claude to autonomously train models, mitigating 10 types of alignment failures such as deception and flattery, significantly reducing the safety gap with perfect performance without compromising general capabilities. The method remains effective on models 4.7 times larger than the optimization target. Claude also surpasses 28 well-known human safety researchers, with its best method in deception scenarios outperforming the best human approach by 20%.","category":"Research","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic allows Claude to autonomously train models to mitigate alignment failures - Aioga AI News","description":"Anthropic allows Claude to autonomously train models, mitigating 10 types of alignment failures such as deception and flattery, significantly reducing the safety gap with perfect p...","url":"https://www.aioga.com/en/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:20:02.822Z"},"ja":{"title":"Anthropic は Claude に自律的にモデルを訓練させ、アラインメント失敗を軽減する","summary":"Anthropic は Claude に自律的にモデルを訓練させ、欺瞞、へつらいなど10種類のアライメント失敗を緩和し、いずれも完璧なパフォーマンスとの安全性の差を著しく縮小し、汎用能力を損なわない方法を提供しています。この方法は、最適化対象の4.7倍大きいモデルでも依然として有効です。Claude はさらに、28人の人間の安全研究者を上回り、欺瞞シナリオにおける最良の手法は人間の最良案より20％優れています。","category":"論文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic は Claude に自律的にモデルを訓練させ、アラインメント失敗を軽減する - Aioga AIニュース","description":"Anthropic は Claude に自律的にモデルを訓練させ、欺瞞、へつらいなど10種類のアライメント失敗を緩和し、いずれも完璧なパフォーマンスとの安全性の差を著しく縮小し、汎用能力を損なわない方法を提供しています。この方法は、最適化対象の4.7倍大きいモデルでも依然として有効です。Claude はさらに、28人の人間の安全研究者を上回り、欺瞞シナリオに...","url":"https://www.aioga.com/ja/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:20:18.986Z"},"ko":{"title":"Anthropic는 Claude가 정렬 실패를 완화하기 위해 스스로 모델을 훈련하도록 합니다","summary":"Anthropic는 Claude가 모델을 자율적으로 학습하도록 하여 사기, 아첨 등 10가지 정렬 실패를 완화하고, 완벽한 성능과의 안전 차이를 크게 줄이면서 일반 능력을 손상시키지 않으며, 이 방법은 최적화 대상보다 4.7배 큰 모델에서도 여전히 효과적이다. Claude는 또한 28명의 인간 안전 연구자를 능가했으며, 사기 시나리오에서 최고의 방법은 인간의 최선 방안보다 20% 좋다.","category":"연구","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic는 Claude가 정렬 실패를 완화하기 위해 스스로 모델을 훈련하도록 합니다 - Aioga AI 뉴스","description":"Anthropic는 Claude가 모델을 자율적으로 학습하도록 하여 사기, 아첨 등 10가지 정렬 실패를 완화하고, 완벽한 성능과의 안전 차이를 크게 줄이면서 일반 능력을 손상시키지 않으며, 이 방법은 최적화 대상보다 4.7배 큰 모델에서도 여전히 효과적이다. Claude는 또한 28명의 인간 안전 연구자를 능가했으며,...","url":"https://www.aioga.com/ko/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:21:10.403Z"},"es":{"title":"Anthropic permite que Claude entrene modelos de manera autónoma para mitigar el fracaso de la alineación","summary":"Anthropic permite que Claude entrene modelos de forma autónoma, mitigando 10 tipos de fallos de alineación como el engaño y la adulación, reduciendo significativamente la brecha de seguridad con un rendimiento perfecto sin perjudicar la capacidad general, y el método sigue siendo eficaz en modelos 4,7 veces más grandes que el objeto de optimización. Claude también supera a 28 reconocidos investigadores de seguridad humanos, y su mejor método en escenarios de engaño es un 20% mejor que la mejor solución humana.","category":"Investigación","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic permite que Claude entrene modelos de manera autónoma para mitigar el fracaso de la alineación - Aioga Noticias de IA","description":"Anthropic permite que Claude entrene modelos de forma autónoma, mitigando 10 tipos de fallos de alineación como el engaño y la adulación, reduciendo significativamente la brecha de...","url":"https://www.aioga.com/es/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:21:07.428Z"},"fr":{"title":"Anthropic permet à Claude de s'entraîner de manière autonome pour atténuer les échecs d'alignement","summary":"Anthropic permet à Claude d'entraîner de manière autonome des modèles, réduisant 10 types d'échecs d'alignement tels que la tromperie et la flatterie, tout en diminuant significativement l'écart de sécurité par rapport à une performance parfaite sans nuire aux capacités générales. La méthode reste efficace sur des modèles 4,7 fois plus grands que l'objet d'optimisation. Claude dépasse également 28 chercheurs humains en sécurité, sa meilleure méthode dans les scénarios de tromperie étant 20 % meilleure que la meilleure solution humaine.","category":"Recherche","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic permet à Claude de s'entraîner de manière autonome pour atténuer les échecs d'alignement - Aioga Actualités IA","description":"Anthropic permet à Claude d'entraîner de manière autonome des modèles, réduisant 10 types d'échecs d'alignement tels que la tromperie et la flatterie, tout en diminuant significati...","url":"https://www.aioga.com/fr/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:22:01.918Z"},"de":{"title":"Anthropic lässt Claude das Modell eigenständig trainieren, um Fehlanpassungen zu verringern","summary":"Anthropic lässt Claude Modelle eigenständig trainieren, um 10 Arten von Alignierungsfehlern wie Täuschung und Schmeichelei zu lindern, wodurch der Sicherheitsabstand zu perfekter Leistung deutlich verringert wird, ohne die allgemeine Fähigkeit zu beeinträchtigen. Die Methode ist auch bei Modellen, die 4,7-mal größer als das Optimierungsziel sind, wirksam. Claude übertrifft außerdem 28 bekannte menschliche Sicherheitsforscher, wobei die beste Methode in Täuschungsszenarien 20 % besser ist als die beste menschliche Lösung.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic lässt Claude das Modell eigenständig trainieren, um Fehlanpassungen zu verringern - Aioga KI-News","description":"Anthropic lässt Claude Modelle eigenständig trainieren, um 10 Arten von Alignierungsfehlern wie Täuschung und Schmeichelei zu lindern, wodurch der Sicherheitsabstand zu perfekter L...","url":"https://www.aioga.com/de/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:21:59.315Z"},"pt-BR":{"title":"A Anthropic permite que Claude treine modelos de forma autônoma para reduzir falhas de alinhamento","summary":"A Anthropic permite que Claude treine modelos de forma autônoma, mitigando 10 tipos de falhas de alinhamento, como engano e bajulação, reduzindo significativamente a diferença de segurança em relação ao desempenho perfeito sem prejudicar a capacidade geral, e o método continua eficaz em modelos 4,7 vezes maiores que os objetos de otimização. Claude também supera 28 renomados pesquisadores de segurança humanos, com seu melhor método em cenários de engano superando a melhor solução humana em 20%.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"A Anthropic permite que Claude treine modelos de forma autônoma para reduzir falhas de alinhamento - Aioga Notícias de IA","description":"A Anthropic permite que Claude treine modelos de forma autônoma, mitigando 10 tipos de falhas de alinhamento, como engano e bajulação, reduzindo significativamente a diferença de s...","url":"https://www.aioga.com/pt-BR/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:22:52.866Z"},"ru":{"title":"Anthropic позволяет Claude самостоятельно обучать модели, чтобы смягчить проблемы с согласованностью","summary":"Anthropic позволяет Claude самостоятельно обучать модели, чтобы смягчить 10 типов проблем согласования, таких как обман и лесть, что значительно сокращает разрыв с идеальной безопасностью и не ухудшает универсальные способности. Метод остается эффективным даже на моделях, в 4,7 раза превышающих целевой объект оптимизации. Claude также превосходит 28 известных исследователей в области безопасности, а его лучший метод в сценариях обмана на 20% лучше, чем лучший человеческий результат.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic позволяет Claude самостоятельно обучать модели, чтобы смягчить проблемы с согласованностью - Aioga Новости ИИ","description":"Anthropic позволяет Claude самостоятельно обучать модели, чтобы смягчить 10 типов проблем согласования, таких как обман и лесть, что значительно сокращает разрыв с идеальной безопа...","url":"https://www.aioga.com/ru/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:22:52.594Z"},"ar":{"title":"تسمح شركة Anthropic لكلاود بتدريب النماذج بشكل مستقل لتخفيف فشل المحاذاة","summary":"شركة Anthropic جعلت Claude يتدرب بشكل مستقل على النماذج، مما يخفف من 10 أنواع من فشل التوافق مثل الخداع والتملق، ويقلص الفجوة الأمنية مع الأداء المثالي بشكل كبير دون الإضرار بالقدرة العامة، والطريقة تبقى فعالة حتى على نموذج أكبر بمقدار 4.7 أضعاف من الهدف الأمثل. كما أن Claude تفوق على 28 باحثًا بشريًا في مجال السلامة، وأفضل طريقة له في سيناريوهات الخداع أفضل بنسبة 20٪ من أفضل حلول البشر.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"تسمح شركة Anthropic لكلاود بتدريب النماذج بشكل مستقل لتخفيف فشل المحاذاة - Aioga أخبار الذكاء الاصطناعي","description":"شركة Anthropic جعلت Claude يتدرب بشكل مستقل على النماذج، مما يخفف من 10 أنواع من فشل التوافق مثل الخداع والتملق، ويقلص الفجوة الأمنية مع الأداء المثالي بشكل كبير دون الإضرار بالقدر...","url":"https://www.aioga.com/ar/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:23:47.873Z"},"hi":{"title":"Anthropic ने Claude को स्वायत्त रूप से मॉडल प्रशिक्षण करने दिया ताकि संरेखण की विफलताओं को कम किया जा सके","summary":"Anthropic ने Claude को स्वतंत्र रूप से मॉडल प्रशिक्षण करने दिया, धोखाधड़ी, चापलूसी और अन्य 10 प्रकार की संरेखण विफलताओं को कम किया, जो सभी सुरक्षा अंतर को शानदार प्रदर्शन के स्तर तक显कर कम कर देते हैं और सामान्य क्षमता को नुकसान नहीं पहुँचाते, यह तरीका 4.7 गुना बड़े मॉडल पर भी प्रभावी है। Claude ने 28 विशिष्ट मानव सुरक्षा शोधकर्ताओं को भी पीछे छोड़ दिया, उसके धोखाधड़ी परिदृश्य के लिए सर्वोत्तम तरीका मानव के सर्वोत्तम समाधान से 20% बेहतर है।","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic ने Claude को स्वायत्त रूप से मॉडल प्रशिक्षण करने दिया ताकि संरेखण की विफलताओं को कम किया जा सके - Aioga AI समाचार","description":"Anthropic ने Claude को स्वतंत्र रूप से मॉडल प्रशिक्षण करने दिया, धोखाधड़ी, चापलूसी और अन्य 10 प्रकार की संरेखण विफलताओं को कम किया, जो सभी सुरक्षा अंतर को शानदार प्रदर्शन के स्तर त...","url":"https://www.aioga.com/hi/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:23:47.713Z"},"it":{"title":"Anthropic consente a Claude di addestrare autonomamente il modello per mitigare i fallimenti di allineamento","summary":"Anthropic consente a Claude di addestrare autonomamente il modello, alleviando 10 tipi di fallimenti di allineamento come inganno e adulazione, riducendo significativamente il divario di sicurezza rispetto a una prestazione perfetta senza compromettere le capacità generali; il metodo è efficace anche su un modello 4,7 volte più grande dell’obiettivo di ottimizzazione. Claude supera inoltre 28 rinomati ricercatori umani in sicurezza, e il suo miglior approccio negli scenari di inganno è superiore del 20% rispetto alla migliore soluzione umana.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic consente a Claude di addestrare autonomamente il modello per mitigare i fallimenti di allineamento - Aioga Notizie IA","description":"Anthropic consente a Claude di addestrare autonomamente il modello, alleviando 10 tipi di fallimenti di allineamento come inganno e adulazione, riducendo significativamente il diva...","url":"https://www.aioga.com/it/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:24:42.180Z"},"nl":{"title":"Anthropic laat Claude zelfstandig modellen trainen om faal in afstemming te verminderen","summary":"Anthropic laat Claude zelfstandig modellen trainen, waardoor 10 soorten aligneringsfouten zoals misleiding en vleierij worden verminderd, wat de veiligheidskloof met perfecte prestaties aanzienlijk verkleint zonder de algemene capaciteiten aan te tasten. De methode is nog steeds effectief op modellen die 4,7 keer groter zijn dan het optimalisatiedoel. Claude overtreft ook 28 bekende menselijke veiligheidsonderzoekers, en de beste methode in misleidingsscenario's van Claude is 20% beter dan het beste menselijke programma.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic laat Claude zelfstandig modellen trainen om faal in afstemming te verminderen - Aioga AI-nieuws","description":"Anthropic laat Claude zelfstandig modellen trainen, waardoor 10 soorten aligneringsfouten zoals misleiding en vleierij worden verminderd, wat de veiligheidskloof met perfecte prest...","url":"https://www.aioga.com/nl/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:24:34.064Z"},"tr":{"title":"Anthropic, Claude'un hizalama hatalarını hafifletmek için kendi başına model eğitmesine izin veriyor","summary":"Anthropic, Claude'un kendi başına model eğitmesini sağlayarak aldatma, yaltaklanma gibi 10 tür uyumsuzluk hatasını hafifletiyor, hepsi mükemmel performans ile güvenlik arasındaki farkı önemli ölçüde azaltıyor ve genel yeteneği zarar görmüyor; yöntem, optimize edilen hedeften 4,7 kat daha büyük modellerde bile etkili. Claude ayrıca 28 ünlü insan güvenlik araştırmacısını geride bırakıyor; aldatma senaryolarındaki en iyi yöntemi insanın en iyi çözümünden %20 daha iyi.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic, Claude'un hizalama hatalarını hafifletmek için kendi başına model eğitmesine izin veriyor - Aioga AI Haberleri","description":"Anthropic, Claude'un kendi başına model eğitmesini sağlayarak aldatma, yaltaklanma gibi 10 tür uyumsuzluk hatasını hafifletiyor, hepsi mükemmel performans ile güvenlik arasındaki f...","url":"https://www.aioga.com/tr/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:25:34.988Z"},"vi":{"title":"Anthropic để Claude tự huấn luyện mô hình nhằm giảm thiểu thất bại trong việc căn chỉnh","summary":"Anthropic để Claude tự huấn luyện mô hình, giảm bớt 10 loại thất bại trong việc đối sánh như lừa dối, nịnh hót, đều làm giảm đáng kể khoảng cách an toàn so với hiệu suất hoàn hảo mà không làm suy giảm khả năng tổng quát, phương pháp này vẫn hiệu quả trên mô hình lớn gấp 4,7 lần so với đối tượng tối ưu hóa. Claude còn vượt trội hơn 28 nhà nghiên cứu an toàn con người, trong các kịch bản lừa dối, phương pháp tốt nhất của nó tốt hơn 20% so với phương án tốt nhất của con người.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic để Claude tự huấn luyện mô hình nhằm giảm thiểu thất bại trong việc căn chỉnh - Tin tức AI Aioga","description":"Anthropic để Claude tự huấn luyện mô hình, giảm bớt 10 loại thất bại trong việc đối sánh như lừa dối, nịnh hót, đều làm giảm đáng kể khoảng cách an toàn so với hiệu suất hoàn hảo m...","url":"https://www.aioga.com/vi/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:25:32.245Z"},"id":{"title":"Anthropic membuat Claude melatih model secara mandiri untuk mengurangi kegagalan penyesuaian","summary":"Anthropic memungkinkan Claude untuk melatih model secara mandiri, mengurangi 10 jenis kegagalan penyelarasan seperti penipuan dan merayu, secara signifikan memperkecil kesenjangan keamanan dengan kinerja sempurna tanpa merusak kemampuan umum, dan metode ini tetap efektif pada model yang 4,7 kali lebih besar dari objek optimisasi. Claude juga melampaui 28 peneliti keamanan manusia terkenal, dengan metode terbaiknya dalam skenario penipuan 20% lebih baik daripada solusi terbaik manusia.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic membuat Claude melatih model secara mandiri untuk mengurangi kegagalan penyesuaian - Berita AI Aioga","description":"Anthropic memungkinkan Claude untuk melatih model secara mandiri, mengurangi 10 jenis kegagalan penyelarasan seperti penipuan dan merayu, secara signifikan memperkecil kesenjangan...","url":"https://www.aioga.com/id/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:26:28.702Z"},"th":{"title":"Anthropic ให้ Claude ฝึกโมเดลด้วยตัวเองเพื่อลดความล้มเหลวในการปรับให้สอดคล้อง","summary":"Anthropic ให้ Claude ฝึกโมเดลด้วยตนเอง เพื่อลดความล้มเหลวในการจัดแนว 10 ประเภท เช่น การโกง การประจบสอพลอ ซึ่งทั้งหมดช่วยลดช่องว่างด้านความปลอดภัยเมื่อเทียบกับประสิทธิภาพที่สมบูรณ์แบบอย่างชัดเจนโดยไม่ทำให้ความสามารถทั่วไปลดลง วิธีการนี้ยังมีประสิทธิภาพบนโมเดลที่ใหญ่กว่าวัตถุประสงค์ในการปรับแต่ง 4.7 เท่า Claude ยังทำได้ดีกว่านักวิจัยด้านความปลอดภัยของมนุษย์ 28 คน วิธีที่ดีที่สุดในสถานการณ์การโกงของมันดีกว่าวิธีที่ดีที่สุดของมนุษย์ 20%","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic ให้ Claude ฝึกโมเดลด้วยตัวเองเพื่อลดความล้มเหลวในการปรับให้สอดคล้อง - ข่าว AI Aioga","description":"Anthropic ให้ Claude ฝึกโมเดลด้วยตนเอง เพื่อลดความล้มเหลวในการจัดแนว 10 ประเภท เช่น การโกง การประจบสอพลอ ซึ่งทั้งหมดช่วยลดช่องว่างด้านความปลอดภัยเมื่อเทียบกับประสิทธิภาพที่สมบูรณ์แ...","url":"https://www.aioga.com/th/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:26:32.069Z"},"pl":{"title":"Anthropic pozwala Claude samodzielnie trenować modele, aby złagodzić niepowodzenia w dopasowaniu","summary":"Anthropic pozwala Claude samodzielnie trenować modele, łagodząc 10 rodzajów niepowodzeń w dostosowaniu, takich jak oszustwo czy pochlebstwo, znacząco zmniejszając lukę bezpieczeństwa w porównaniu do idealnej wydajności, przy czym nie szkodzi to uniwersalnym zdolnościom. Metoda jest skuteczna także w modelach 4,7 razy większych niż optymalizowany obiekt. Claude przewyższa również 28 czołowych badaczy bezpieczeństwa ludzi, a jego najlepsza metoda w scenariuszach oszustwa jest o 20% lepsza niż najlepsze podejście człowieka.","category":"论文研究","source":"Anthropic：Research（发表成果 · 网页）","aggregationSource":"Anthropic：Research（发表成果 · 网页）","pageTitle":"Anthropic pozwala Claude samodzielnie trenować modele, aby złagodzić niepowodzenia w dopasowaniu - Aioga Wiadomości AI","description":"Anthropic pozwala Claude samodzielnie trenować modele, łagodząc 10 rodzajów niepowodzeń w dostosowaniu, takich jak oszustwo czy pochlebstwo, znacząco zmniejszając lukę bezpieczeńst...","url":"https://www.aioga.com/pl/news/cmtd83hb4018fro667i1tbc34/","contentTranslated":true,"sourceHash":"60fc2d543730d40f","translatedAt":"2026-08-29T17:27:32.126Z"}},"evidenceTier":"verified-news","reviewStatus":"editorial-selected","indexable":true,"editorialCover":""}}