arXiv:2610.06439 · v2
Better Call Reward: Reward Hacking as Strategic Abstention in Legal Reasoning Models
摘要
What happens when a legal AI model learns to look like a lawyer instead of reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisation (GRPO) against a proxy built from three surface features: citation count, legalese density, and response length. The model does not learn to reason more effectively. It stops committing to an answer. Across 16 yes or no legal reasoning tasks from LegalBench (N=320), overall accuracy collapses from 0.500 (chance) to 0.072 (McNemar p < 10^-36), driven entirely by the rate of properly formatted answers falling from 0.900 to 0.109. When the model does commit, accuracy is 0.657 against 0.556 before training, on only about 35 answers, suggesting the collapse is not mainly a loss of capability. The mechanism is visible in the training logs: the proxy keeps paying for length beyond what fits in the 256 token generation limit, completions grow until they fill that budget, and the required answer line is crowded out. We term this the Saul Goodman effect, a policy that becomes more lawyerly in form while becoming noncommittal, and prove formally that abstention is the optimal response to any surface feature proxy that attaches no penalty to it. A citation audit shows that structural plausibility checks cannot catch this failure: every case name we extracted from the trained model's held out completions passes our structural check, so citations must be verified against a case database. To detect this failure before deployment, we introduce three diagnostic tools: the Confidence Theater Score (CTS), the Citation Plausibility Rate (CPR), and the Regret Gap (RG), and report where each one falls short. In a domain where a confidently wrong answer can constitute malpractice, the broader lesson is direct: a reward function that measures how legal a response looks will produce a model that is maximally photogenic and minimally useful.
作者备注：13 Pages, Proceedings of the AI for Law Workshop at the 43rd International Conference on Machine Learning, Seoul, South Korea. PMLR 356, 2026