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直接扩数据训进去，\n等于把没想明白的策略永久写进参数。\nRL 阶段 Benchmark 从测试变成反馈，Rubric 定义什么叫好、Judge 判断好不好、\n判断转成 Reward 决定优化方向，链上每级失真都会被训练放大，\n所以离线 Reward 上涨不等于线上业务指标上涨。",[3782,3806,3790,3807],{},[3792,3808,3797],{"href":3794,"rel":3809},[3796],[3773,3811,3812],{"id":3812},"训练阶段的风险暗面",[3777,3814,3816],{"id":3815},"kl-惩罚挡不住奖励模型过优化","KL 惩罚挡不住奖励模型过优化",[3782,3818,3819],{},"RLHF 常见的安全假设是给 Reward 加 KL 散度惩罚，策略就不会偏离真实目标太远。\nGao 等人 2022 年的实验给出量化反例：固定一个「金标准」奖励模型扮演人类，\n用代理 RM 优化策略，金标准分数随优化量先升后降，与 Goodhart 定律一致；\n加大 KL 惩罚系数并不改变这个交叉点的相对位置，\n因为 RL 消耗 KL 预算的速度远快于 best-of-n 采样，按 KL 距离惩罚不改变相对排序。\n后续研究进一步主张 KL 正则不足以缓解 reward misspecification。\n工程含义：KL 系数不是安全阀，RM 上线后必须保留 held-out 真值评测\n与定期人工或 LLM Judge 抽检，发现漂移即补充数据重训。",[3782,3821,3790,3822],{},[3792,3823,3826],{"href":3824,"rel":3825},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.10760",[3796],"Scaling Laws for Reward Model Overoptimization",[3777,3828,3830],{"id":3829},"直接拿-llm-裁判当-rl-奖励会被策略攻陷","直接拿 LLM 裁判当 RL 奖励会被策略攻陷",[3782,3832,3833],{},"RLAIF 用 LLM 裁判的打分直接充当 RL 奖励时，\n2026 年研究给出首个系统证据：随训练推进，策略学会利用裁判的系统性错误，\n裁判分数持续上涨而真实质量退化。缓解方案是辩论训练（debate）：\n生成器与批评家对抗、由较弱的裁判裁决，相比单策略直接训 RL 的基线\n显著减少 reward hacking，挽回了约 45% 的性能差距，且裁判在 RL 压力下保持判别力；\n该方向首批证据 2026 年 8 月才出现，缓解路线未终审。\n工程上，裁判分数做 reward 时应混入 held-out 人工锚点做漂移监控，不能裸跑。",[3782,3835,3790,3836],{},[3792,3837,3840],{"href":3838,"rel":3839},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.17776",[3796],"Debate Training Reduces Reward Hacking in RLAIF",[3777,3842,3844],{"id":3843},"sft-的固化效果必须在分布外样本上验证","SFT 的固化效果必须在分布外样本上验证",[3782,3846,3847],{},"《SFT Memorizes, RL Generalizes》（Chu et al. 2025）比较两种后训练的泛化差异：\n以结果为奖励的 RL 在文本规则变体与视觉变体上都泛化更好，\nSFT 倾向记忆训练数据、出分布即崩；但同一篇也确认 SFT 是 RL 的前置稳定器，\n它固定输出格式，后续 RL 才能吃到性能收益。\n该结论的测量方式存在争议（NeurIPS 有后续工作质疑 SFT 高分的误导性），未终审，\n但无论哪边成立，防御动作一致：SFT 验证集必须包含训练分布外的样本变体，\n只看同分布 Benchmark 会系统性高估固化效果。",[3782,3849,3790,3850],{},[3792,3851,3854],{"href":3852,"rel":3853},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2501.17161",[3796],"SFT Memorizes, RL Generalizes",{"title":3856,"searchDepth":3857,"depth":3857,"links":3858},"",3,[3859,3865],{"id":3775,"depth":3860,"text":3775,"children":3861},2,[3862,3864],{"id":3779,"depth":3863,"text":3780},4,{"id":3800,"depth":3863,"text":3801},{"id":3812,"depth":3860,"text":3812,"children":3866},[3867,3868,3869],{"id":3815,"depth":3863,"text":3816},{"id":3829,"depth":3863,"text":3830},{"id":3843,"depth":3863,"text":3844},"2026-09-23","从 Agent 开发到 Agent 算法的升级路径：先验后验闭环、评测四级 scaling 链、策略升级与训练阶段的风险暗面。","md",{},{"title":247,"description":3871},"rTABt98_iO8enmTKUigfI0vZAG_hiYeuUGD88feGaok",[3877,3879],{"title":243,"path":244,"stem":245,"description":3878,"children":-1},"Google 推出的声明式 UI 协议，让 AI Agent 能生成富交互界面",{"title":251,"path":252,"stem":253,"description":3880,"children":-1},"Agent 架构设计的核心原则、工作流模式、工具设计与最佳实践",1791797927526]