Inference-Time Diversity in RL-Trained Lean Theorem Provers: A Diagnostic Study

Zachary Burton

Code Url: https://github.com/zacn04/icml_hints_for_provers

Keywords: Neural Theorem Proving, Lean4, Reinforcement Learning

TL;DR: RL trained theorem provers strategically, but fatally, explore a small slice of the available tactic space at inference time; hints are an easy way to recover what simple sampling cannot.

Abstract: RL-trained Lean theorem provers mode-collapse at inference time: on miniF2F-test with DeepSeek-Prover-V1.5-RL, doubling the i.i.d.\ sampling budget from $k{=}32$ to $k{=}64$ produces zero additional solved theorems (42/244 in both cases). A fixed schedule of 15 tactic skeletons breaks this plateau and recovers a $+45\%$ relative improvement at $k{=}16$ (mean $\Delta = +12.3 \pm 4.2$ theorems across $n{=}3$ seeds, sign preserved in every seed). A controlled diversity ablation rules out the prompt-diversity confound: tactic skeletons help, paraphrases match the baseline, and irrelevant Lean comments actively degrade. A leave-one-out formalization-difficulty stratification reveals a structural-content gradient across the three perturbations. The phenomenon is RL-specific: V1.5-Base proves zero theorems regardless of intervention, identifying RL as the stage that creates the proof capability which subsequently collapses; extending to two additional 7B Lean provers, RL-trained DeepSeek-Prover-V2-7B contributes $+3$ frontier solves no i.i.d.\ baseline can reach despite a flat aggregate, while SFT-trained Goedel-Prover does not ($-10.0 \pm 4.4$ theorems, $n{=}3$, sign preserved every seed). Inference-time structural diversity is a cheap, complementary axis for RL-trained provers, orthogonal to scaling model size or training compute.

Submission Number: 72