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cs.LG2026
CRAFT: Counterfactual Credit Assignment from Free Sibling Rollouts for Self-Distilled Agentic Reinforcement Learning
Zibin Meng, Kani Chen
Self-distilled agentic reinforcement learning augments trajectory-level reward with a token-level distillation loss, using as its teacher the same policy conditioned on privileged…
cs.LG2026
Depth-Entropy Guided Sampling for Training-Free LLM Reasoning
Zibin Meng, Peng Xie, Kani Chen
Reinforcement learning (RL) has become the dominant paradigm for improving the reasoning capabilities of large language models, but it requires expensive training, curated data, an…