4 papers · 1 filter
LatentRevise: Learning from Zero-Hit Reasoning
Yiqiu Guo, Xueting Han, Qi Jia +2
Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by hard prompts on which correct trajectories have low probability, so sampling misses them within a practical…
Beyond Two-Stage Training: Cooperative SFT and RL for LLM Reasoning
Liang Chen, Xueting Han, Li Shen +2
Supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) are two widely used post-training paradigms for improving the reasoning ability of large lang…
EEPO: Exploration-Enhanced Policy Optimization via Sample-Then-Forget
Liang Chen, Xueting Han, Qizhou Wang +4
Balancing exploration and exploitation remains a central challenge in reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs). Current RLVR methods o…
NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models
Shengrui Li, Junzhe Chen, Xueting Han +1
The considerable size of Large Language Models (LLMs) presents notable deployment challenges, particularly on resource-constrained hardware. Structured pruning, offers an effective…