| Challenge: | Evaluating the quality of LLM-generated reasoning traces in expert domains is essential for ensuring credibility and explainability, yet remains challenging due to the inherent complexity of such reasoning tasks. |
| Approach: | They propose a large-scale legal reasoning dataset with an emphasis on reasoning trace evaluation that converts court judgments into hierarchical trees of opposing parties’ arguments and the court’s conclusions. |
| Outcome: | The proposed model improves the quality of LLM-generated reasoning traces in legal domains, whereas RL improves correctness albeit with reduced coverage. |
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