4 papers
One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement
Yixiao Zhou, Dongzhou Cheng, zhiliang wu +3
Large Language Models (LLMs) often fail to utilize their latent reasoning capabilities due to a distributional mismatch between ambiguous human inquiries and the structured logic r…
Look Inward to Explore Outward: Learning Temperature Policy from LLM Internal States via Hierarchical RL
Yixiao Zhou, Yang Li, Dongzhou Cheng +2
Reinforcement Learning from Verifiable Rewards (RLVR) trains large language models (LLMs) from sampled trajectories, making decoding strategy a core component of learning rather th…
RLFR: Extending Reinforcement Learning for LLMs with Flow Environment
Jinghao Zhang, Naishan Zheng, Ruilin Li +4
Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a promising framework for improving reasoning abilities in Large Language Models (LLMs). However, poli…
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs
Yixiao Zhou, Ziyu Zhao, Dongzhou Cheng +6
Sparse Mixture-of-Experts (SMoE) architectures are widely used in large language models (LLMs) due to their computational efficiency. However, though only a few experts are activat…