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cs.LG2026
Information-Time Proximal Policy Optimization
Yongcheng Zeng, Xinyu Cui, Yan Song +9
RLVR has substantially improved the reasoning capabilities of LLMs. However, existing methods typically parameterize temporal progression in the Markov Decision Process by token-by…
cs.LG2026
TD-Grokking: Learning from Zero-Reward Problems by Training-Time Decomposition
Ningyuan Xi, Hao Xu, Hongsheng Xin +1
Large language models (LLMs) have made remarkable progress in reasoning tasks, largely driven by post-training paradigms, especially reinforcement learning with verifiable rewards…
cs.LG2026
HMPO: Hybrid Median-length Policy Optimization for Chain-of-Thought Compression
Minghui Zheng, Hongxu Chen, Huimin Ren +8
Large language models achieve remarkable performance via extended chain-of-thought (CoT) reasoning, yet this lengthy process incurs substantial inference overhead. Existing CoT com…