Papers by En Wang
Token-Level Policy Optimization: Linking Group-Level Rewards to Token-Level Aggregation via sequence-level likelihood (2026.acl-long)
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| Challenge: | Group Relative Policy Optimization (GRPO) has significantly advanced the reasoning ability of large language models (LLMs). |
| Approach: | They propose a token-level framework that leverages sequence-level likelihood to link group-level rewards with individual tokens via token- level aggregation and introduces a KL-Divergence mask constraint that targets tokens with positive advantages and decreasing entropy to mitigate abrupt policy updates. |
| Outcome: | Experiments show that TEPO achieves state-of-the-art performance on mathematical reasoning benchmarks and reduces convergence time by 50% compared with GRPO/DAPO. |
RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios (2025.acl-long)
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| Challenge: | RuleArena assesses the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. |
| Approach: | They propose a benchmark to evaluate the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. |
| Outcome: | The proposed benchmark covers airline baggage fees, NBA transactions, and tax regulations. |
Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction (2025.naacl-long)
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| Challenge: | Extractive UIEs can solve model explosion problems using a relatively small model . single-target instruction UIE enables the extraction of only one type of relation at a time . |
| Approach: | They propose a model that assigns different relations to different levels for understanding and decision-making. |
| Outcome: | Experiments show that LDNet outperforms state-of-the-art systems on 9 tasks, 33 datasets . LDnet outperformed state- of-the art systems on single-modal and multi-modal tasks . |