1 citations · 1 across the 10 of their papers we have counts for
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Towards a Theoretical Understanding to the Generalization of RLHF
Zhaochun Li, Mingyang Yi, Yue Wang +2
Reinforcement Learning from Human Feedback (RLHF) and its variants have emerged as the dominant approaches for aligning Large Language Models with human intent. While empirically e…
ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment
Xiuyu Li, Jinkai Zhang, Mingyang Yi +4
Reinforcement Learning (RL) post-training alignment for language models is effective, but also costly and unstable in practice, owing to its complicated training process. To addres…
Stabilizing Policy Gradient Methods via Reward Profiling
Shihab Ahmed, El Houcine Bergou, Aritra Dutta +1
Policy gradient methods, which have been extensively studied in the last decade, offer an effective and efficient framework for reinforcement learning problems. However, their perf…
DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management
Xuerui Su, Liya Guo, Yue Wang +4
Inference scaling further accelerates Large Language Models (LLMs) toward Artificial General Intelligence (AGI), with large-scale Reinforcement Learning (RL) to unleash long Chain-…
Symbolic Representation for Any-to-Any Generative Tasks
Jiaqi Chen, Xiaoye Zhu, Yue Wang +9
We propose a symbolic generative task description language and a corresponding inference engine capable of representing arbitrary multimodal tasks as structured symbolic flows. Unl…
Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning
Xuerui Su, Shufang Xie, Guoqing Liu +7
Recently, Large Language Models (LLMs) have rapidly evolved, approaching Artificial General Intelligence (AGI) while benefiting from large-scale reinforcement learning to enhance H…