Papers by Aochong Oliver Li
Better LLM Reasoning via Dual-Play (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have made remarkable progress through Reinforcement Learning with Verifiable Rewards (RLVR) however, external supervision remains a bottleneck for tasks and domains for which supervised data are scarce or non-existent. |
| Approach: | They propose a novel dual-play framework that adversarially trains two models initialized from the same base model. |
| Outcome: | The proposed framework improves the math reasoning performance of large language models. |
Memorization vs. Reasoning: Updating LLMs with New Knowledge (2025.findings-acl)
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| Challenge: | Existing methods and benchmarks focus on entity substitutions, failing to capture the full breadth of complex real-world dynamics. |
| Approach: | They propose an automatic pipeline for simulating realistic knowledge updates reflected in an evidence corpus. |
| Outcome: | The proposed method outperforms prior continued pre-training (CPT) baselines on two LLM families and improves direct probing (memorization) results by 25.4%. |