6 papers
Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning
Haolin Liu, Dian Yu, Sidi Lu +6
Reinforcement learning (RL) has emerged as a powerful framework for improving the reasoning capabilities of large language models (LLMs). However, most existing RL approaches rely…
RelayLLM: Efficient Reasoning via Collaborative Decoding
Chengsong Huang, Tong Zheng, Langlin Huang +3
Large Language Models (LLMs) for complex reasoning is often hindered by high computational costs and latency, while resource-efficient Small Language Models (SLMs) typically lack t…
Stable and Efficient Single-Rollout RL for Multimodal Reasoning
Rui Liu, Dian Yu, Lei Ke +6
Reinforcement Learning with Verifiable Rewards (RLVR) has become a key paradigm to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, prevalen…
CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
Runpeng Dai, Linfeng Song, Haolin Liu +8
Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm for enhancing the reasoning ability of Large Language Models (LLMs). Yet current RLVR methods often exp…
Decision Making in Hybrid Environments: A Model Aggregation Approach
Haolin Liu, Chen-Yu Wei, Julian Zimmert
Recent work by Foster et al. (2021, 2022, 2023b) and Xu and Zeevi (2023) developed the framework of decision estimation coefficient (DEC) that characterizes the complexity of gener…
Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback
Haolin Liu, Zakaria Mhammedi, Chen-Yu Wei +1
We consider regret minimization in low-rank MDPs with fixed transition and adversarial losses. Previous work has investigated this problem under either full-information loss feedba…