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The paper proposes Reasoning-Progress-Aware Reward Filtering for On-Policy Distillation (R2-OPD) to address the mismatch between teacher-derived rewards and genuine reasoning progress, improving reasoning performances in language model training.
This paper proposes Adaptive Entropy Distillation (AED), a method that dynamically calibrates token-level imitation strength in knowledge distillation using teacher entropy, achieving superior performance on instruction-following and mathematical reasoning benchmarks.
ReflectRL is a framework that learns from 'golden negative trajectories' (failed reasoning attempts by expert models) by reflecting on them, then transfers this reflective reasoning back to direct reasoning, improving LLM performance across benchmarks.
This paper introduces On-Policy Data Evolution (ODE) and a visual-native agent harness to improve multimodal deep search agents. By enabling reusable visual evidence and closed-loop data generation, ODE significantly boosts the performance of Qwen3-VL agents across multiple benchmarks, surpassing Gemini 2.5 Pro.