3 papers
cs.LG2026
Breaking the Capability Ceiling of LLM Post-Training by Reintroducing Markov States
Yurun Yuan, Tengyang Xie
Reinforcement learning (RL) has become a standard paradigm for post-training and aligning Large Language Models (LLMs), yet recent evidence suggests it faces a persistent "capabili…
cs.LG2025
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning
Yurun Yuan, Fan Chen, Zeyu Jia +2
Policy-based methods currently dominate reinforcement learning (RL) pipelines for large language model (LLM) reasoning, leaving value-based approaches largely unexplored. We revisi…
cs.LG2025
Reinforce LLM Reasoning through Multi-Agent Reflection
Yurun Yuan, Tengyang Xie
Leveraging more test-time computation has proven to be an effective way to boost the reasoning capabilities of large language models (LLMs). Among various methods, the verify-and-i…