Papers by Yangen Hu

2 papers
Reward Alignment Optimization: A Direct Point-wise Alignment Approach (2026.acl-long)

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Challenge: Existing Direct Alignment Algorithms (DAAs) are limiting in generalizaiton to implicit rewards.
Approach: They propose a point-wise direct alignment method that uses an explicit reward model to specify exact target generation probabilities and align the policy offline towards them.
Outcome: The proposed method outperforms existing direct alignment algorithms while enabling controllable target probability distributions.
Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs (2025.emnlp-main)

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Challenge: Existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient.
Approach: They propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training.
Outcome: The proposed model improves response precision while preserving exploratory ability to uncover potential correct pathways.

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