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ARXIV:2605.10141 · FORMAL THEOREM PROVING AI · SUBMITTED 12 MAY · 20:15 UTC · FRESHNESS FRESH
ARXIV:2605.10141FORMAL THEOREM PROVING AISUBMITTED 12 MAY · 20:15 UTCFRESHNESS FRESHZeynel A. Uluşan · Burak S. Akbudak · Can S. Erer · Gözde Gül Şahin · arXiv
A benchmark for evaluating reward models in formal theorem proving, crucial for advancing AI assistants in complex mathematical reasoning.
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Pain A benchmark for evaluating reward models in formal theorem proving, crucial for advancing AI assistants in complex mathematical reasoning.
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A benchmark for evaluating reward models in formal theorem proving, crucial for advancing AI assistants in complex mathematical reasoning. While verifiable rewards are cheap and scalable without reward hacking issues, they suffer from sparse…
Recent neural theorem provers use reinforcement learning with verifiable rewards (RLVR), where proof assistants provide binary correctness signals. While verifiable rewards are cheap and scalable without reward hacking issues, they suffer from sparse credit…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results reveal that frontier LLMs achieve the highest performance (59.8\%) while specialized theorem provers perform the worst (24.4\%), suggesting that theorem proving ability…
Formal Theorem Proving AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A benchmark for evaluating reward models in formal theorem proving, crucial for advancing AI assistants in complex mathematical reasoning.
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10.48550/arXiv.2605.10141A benchmark for evaluating reward models in formal theorem proving, crucial for advancing AI assistants in complex mathematical reasoning.
Abstract
Recent neural theorem provers use reinforcement learning with verifiable rewards (RLVR), where proof assistants provide binary correctness signals. While verifiable rewards are cheap and scalable without reward hacking issues, they suffer from sparse credit assignment: models receive no learning signal from difficult problems where partial progress goes unrewarded. This motivates learned reward models that can evaluate proof quality beyond binary verification. However, comparing reward models is challenging since it typically requires expensive RL training ablations. To address this, we introduce \textbf{FormalRewardBench}, the first benchmark for evaluating reward models in formal theorem proving with Lean 4. Our benchmark consists of 250 preference pairs where correct proofs are paired with incorrect variants generated through five expert curated error injection strategies: forced mistakes, minimal single-point variations, verbose incorrect proofs, natural language justification, and Python code injection. We evaluate frontier LLMs (e.g., Claude Opus 4.5), judge LLMs (e.g., CompassJudger-1-14B), general-purpose LLMs (e.g., Qwen2.5-72B-Instruct), and specialized theorem proving models (e.g., DeepSeek-Prover-V2-7B). Our results reveal that frontier LLMs achieve the highest performance (59.8\%) while specialized theorem provers perform the worst (24.4\%), suggesting that theorem proving ability does not transfer to proof evaluation. We provide further insights on various error injection mechanisms, highlighting the challenging nature of most injection mechanisms. We release \textbf{FormalRewardBench} publicly to encourage more research on developing reward models in formal mathematics.
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PROBLEM
A benchmark for evaluating reward models in formal theorem proving, crucial for advancing AI assistants in complex mathematical reasoning. While verifiable rewards are cheap and scalable without reward hacking issues, they suffer from sparse credit assignment: models receive no...
METHOD
Recent neural theorem provers use reinforcement learning with verifiable rewards (RLVR), where proof assistants provide binary correctness signals. While verifiable rewards are cheap and scalable without reward hacking issues, they suffer from sparse credit assignment: models re...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results reveal that frontier LLMs achieve the highest performance (59.8\%) while specialized theorem provers perform the worst (24.4\%), suggesting that theorem proving ability does not transfer to pr...
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Formal Theorem Proving AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A benchmark for evaluating reward models in formal theorem proving, crucial for advancing AI assistants in complex mathematical reasoning. While verifiable rewards are cheap and scalable without reward hacking issues, they suffer from sparse credit assignment: models receive no learning signal from difficult problems where partial progress goes unrewarded.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Recent neural theorem provers use reinforcement learning with verifiable rewards (RLVR), where proof assistants provide binary correctness signals. While verifiable rewards are cheap and scalable without reward hacking issues, they suffer from sparse credit assignment: models receive no learning signal from difficult problems where partial progress goes unrewarded.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results reveal that frontier LLMs achieve the highest performance (59.8\%) while specialized theorem provers perform the worst (24.4\%), suggesting that theorem proving ability does not transfer to proof evaluation. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Formal Theorem Proving AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A benchmark for evaluating reward models in formal theorem proving, crucial for advancing AI assistants in complex mathematical reasoning.
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Formal Theorem Proving AI
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