1 citations · 1 across the 14 of their papers we have counts for
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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…
An Improved Model-Free Decision-Estimation Coefficient with Applications in Adversarial MDPs
Haolin Liu, Chen-Yu Wei, Julian Zimmert
We study decision making with structured observation (DMSO). Previous work (Foster et al., 2021b, 2023a) has characterized the complexity of DMSO via the decision-estimation coeffi…
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…
Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation
Yujun Zhou, Zhenwen Liang, Haolin Liu +7
Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR), yet real-world deployment demands models that can self-improve wit…
Moving Out: Physically-grounded Human-AI Collaboration
Xuhui Kang, Sung-Wook Lee, Haolin Liu +2
The ability to adapt to physical actions and constraints in an environment is crucial for embodied agents (e.g., robots) to effectively collaborate with humans. Such physically gro…
One Token to Fool LLM-as-a-Judge
Yulai Zhao, Haolin Liu, Dian Yu +4
Large language models (LLMs) are increasingly trusted as automated judges, assisting evaluation and providing reward signals for training other models, particularly in reference-ba…