Papers by Lina Sun
ReActR: Reasoning through Error-Activated Reflection for LLM Post-Training (2026.acl-long)
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| Challenge: | Existing methods for improving the mathematical abilities of Large Language Models (LLMs) focus disproportionately on scaling correct training samples, overlooking the rich learning signals contained in erroneous reasoning trajectories. |
| Approach: | They propose a framework that enhances reasoning by learning reflective behaviors from erroneous trajectories by using data construction and training. |
| Outcome: | Extensive experiments on three LLMs show that ReActR improves reasoning performance on Llama-3-8B. |