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cs.LG2026
Self-Supervised On-Policy Distillation for Reasoning Language Models
Zhiquan Tan, Yinrong Hong
GRPO-style RLVR trains reasoning models from multiple on-policy attempts per prompt, but typically uses these attempts only through terminal rewards. We show that a mixed group con…
cs.LG2026
PAINT: Partial-Solution Adaptive Interpolated Training for Self-Distilled Reasoners
Zhiquan Tan, Yinrong Hong
Improving large language model (LLM) reasoning requires supervision that is both aligned with the model's own test-time states and informative at the token level. Reinforcement lea…
cs.LG2025
A Theoretical Lens for RL-Tuned Language Models via Energy-Based Models
Zhiquan Tan, Yinrong Hong
Large language models (LLMs) trained via KL-regularized reinforcement learning demonstrate strong instruction following, self-correction, and reasoning abilities. Yet their theoret…