VeriFine: Scaling Verification for Self-Improvement in Embodied Reasoning· VeriFine：在具身推理中扩展验证以实现自我改进
Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges constrain both optimization feedback and the discovery of useful training examples, limiting further self-improvement. This challenge is even more acute in embodied reasoning, where reliable evaluation must account for spatial grounding, causal reasoning, and safety-aware decision-making. We introduce VeriFine, an agent harness framework that scales verification through the co-evolution of the policy, training curriculum, and judge. The Policy Improvement Loop uses a rubric judge to diagnose recurring failures, construct an adaptive curriculum, and optimize the policy. When progress plateaus and verification becomes a bottleneck, the Judge Improv
VeriFine 通过策略、训练课程和裁判的共同进化扩展具身推理中的自我改进验证。
- 核心方法
- VeriFine 引入了策略改进循环，使用评分裁判来诊断常见失败，构建自适应训练课程，并优化策略，以克服验证瓶颈。
- 适合谁读
- 研究者
- 要解决的问题
- 当前固定的裁判限制了具身推理模型的优化反馈和有效训练样本的发现，阻碍了模型的持续自我改进。
- 关键实验
- 未提供
- 主要贡献
- 提出了VeriFine框架，实现了策略、训练课程和裁判的共同进化，显著提升了具身推理模型的自我改进能力。
- 意义与局限
- VeriFine 为具身推理系统的持续优化提供了一种有效的方法，但其效果和通用性需要通过更多实验来验证。