CovenantBench: Navigating Corporate Debt Contracts with Litigation-Verified Rewards

We present CovenantBench, a benchmark and reinforcement-learning environment for legal reasoning over corporate debt instruments, anchored to verifiable outcomes of real covenant litigation. The benchmark has three layers: (i) agentic document-navigation tasks over actual indentures and credit agreements pulled from public filings — a 4,042-task evaluation suite with audited keys — scored by deterministic checkers; (ii) outcome prediction on real covenant disputes, with a hand-audited held-out evaluation set — 101 issue prompts across 89 disputes, every record reviewed line-by-line against the deciding opinion — each labeled by the recorded outcome and the kind of motion or appeal the court was deciding; and (iii) an adversarial debate protocol in which advocate policies argue both sides of contested clauses before a judge whose reward is anchored to the true outcome. We train a 9B-parameter model (QLoRA + GRPO, under $150ofcompute)throughsupervisedtracing,navigationRL,judgeanchoring,andtworoundsofdebateself-play.Threefindingsfollow.First,the$150 of training pays for itself across every layer: it doubles zero-shot agentic pass@1 on 825 held-out tasks of its training families (0.283 →0.558 composite, 0.234 →0.485 fullmarks, thrice re-evaluated), lifts the base on every family of the audited evaluation suite, and matches frontier-scale models on outcome prediction at roughly one-fifth their inference cost. Second, the trained judge’s predictions respond to the clause, not just the facts: holding the facts fixed and outcome-scrubbed, swapping a protective clause for a permissive one moves P(creditor) in the doctrinally correct direction. Third, our arms do not answer from memory, while frontier arms partly do: closed-book probes place both our base and trained models at chance, while the comparison model 44× larger in total parameters recites actual rulings from memory. We release the corpus, environment, checkers, and adapters.