LegalEvolver: Agentic Legal Judgment Prediction via Rubric-Guided Self-Evolution

LegalEvolver is a query‑optimization framework for legal judgment prediction that generates legally informative queries to improve retrieval quality. It uses heuristic query seeding, summarizes rollout experiences into rubrics, and applies rubric‑guided reinforcement learning for iterative self‑evolution. Experiments on large benchmarks show that evolving rubrics outperform fixed ones and that rubric guidance boosts performance beyond direct supervised fine‑tuning plus RL.

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• llms
• agents
• reinforcement-learning
• fine-tuning
• benchmarks