mlsanityData to 7 Oct 2026

Evaluating the Role of Verifiers in Test-Time Scaling for Legal Reasoning Tasks

arXiv 2510.25623 ↗First posted 29 Oct 2025cs.CLcited by 0

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

Test-time scaling (TTS) techniques can improve the performance of large language models (LLMs) at the expense of additional computation and latency. While TTS has proven effective in formal domains such as mathematics and programming, its value in argumentative domains such as law remains underexplored. We present an empirical study of verifier-based TTS methods for legal multiple-choice QA (MCQA) across five benchmarks. Using a family of 7 reward models, we evaluate both outcome-level (Best-of-N) and process-level (tree search) verification under realistic low-N budgets. Our analysis systematically investigates how verifier utility is affected by key properties such as domain specialization, model size, and supervision type (process-supervised PRMs vs. outcome-only ORMs), even when applied across different roles.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 14 of its 20 references that are in the map. First posted on arXiv 29 Oct 2025.

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14 of 20 references.

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  1. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters6 Aug
  2. 2024Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models1 Aug

Other references · 12

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  2. 2024OpenAI o1 System Cardcited by 1,220
  3. 2024Large Language Monkeys: Scaling Inference Compute with Repeated Samplingcited by 946
  4. 2022Solving math word problems with process- and outcome-based feedbackcited by 871
  5. 2024RewardBench: Evaluating Reward Models for Language Modelingcited by 480
  6. 2025The Lessons of Developing Process Reward Models in Mathematical Reasoningcited by 402
  7. 2024Skywork-Reward: Bag of Tricks for Reward Modeling in LLMscited by 322
  8. 2025SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplinescited by 67
  9. 2025LEXam: Benchmarking Legal Reasoning on 340 Law Examscited by 52
  10. 2025VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Datacited by 39
  11. 2024Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systemscited by 24
  12. 2025Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyondcited by 9

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  1. 2025Trust but Verify! A Survey on Verification Design for Test-time Scaling
  2. 2025Scaling Flaws of Verifier-Guided Search in Mathematical Reasoning
  3. 2025When Does Verification Pay Off? A Closer Look at LLMs as Solution Verifiers
  4. 2025Variation in Verification: Understanding Verification Dynamics in Large Language Models
  5. 2025Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning
  6. 2025The Art of Scaling Test-Time Compute for Large Language Models
  7. 2025A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?
  8. 2026When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning