增强证据的策略优化用于长上下文推理
Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning
AI总结：
本文提出EAPO方法，通过引入组相对证据奖励和适应性奖励-策略共演机制，提升长上下文推理中的证据提取质量，从而增强推理性能。
摘要：
尽管强化学习（RL）已推动大语言模型（LLM）推理能力的进步，但在长上下文场景中应用RL受到回报稀疏性的限制。这种限制未能惩罚不切实际的
英文摘要：
While Reinforcement Learning (RL) has advanced LLM reasoning, applying it to long-context scenarios is hindered by sparsity of outcome rewards. This limitation fails to penalize ungrounded "lucky guesses," leaving the critical process of needle-in-a-haystack evidence retrieval largely unsupervised. To address this, we propose EAPO (Evidence-Augmented Policy Optimization). We first establish the Evidence-Augmented Reasoning paradigm, validating via Tree-Structured Evidence Sampling that precise evidence extraction is the decisive bottleneck for long-context reasoning. Guided by this insight, EAPO introduces a specialized RL algorithm where a reward model computes a Group-Relative Evidence Reward, providing dense process supervision to explicitly improve evidence quality. To sustain accurate supervision throughout training, we further incorporate an Adaptive Reward-Policy Co-Evolution mechanism. This mechanism iteratively refines the reward model using outcome-consistent rollouts, sharpening its discriminative capability to ensure precise process guidance. Comprehensive evaluations across eight benchmarks demonstrate that EAPO significantly enhances long-context reasoning performance compared to SOTA baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
