Papers by Jia Leng
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. |
Token-Wise Kernels (TWiKers) for Vicinity-Aware Attention in Transformers (2026.findings-eacl)
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| Challenge: | Token-Wise Kernels (TWiKers) are a novel enhancement to transformers that learn token-specific convolutional kernels applied to the keys or values. |
| Approach: | They propose a transformer enhancement that learns token-specific convolutional kernels applied to the keys or values. |
| Outcome: | The proposed transformers learn token-specific convolutional kernels applied to the keys or values . the results show that content words retain self-focus while function words shift attention toward their neighbors . |
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. |
Neural Topic Model with Reinforcement Learning (D19-1)
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| Challenge: | Experimental results show superior performance on perplexity and topic coherence measures compared to state-of-the-art topic models. |
| Approach: | They propose to incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model. |
| Outcome: | The proposed model is able to separating background words dynamically from topic words eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models. |