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Scaling Test-Time Compute Without Verification or RL is Suboptimal

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Verifier-based methods outperform verifier-free distillation for scaling test-time compute in LLMs

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About Scaling Test-Time Compute Without Verification or RL is Suboptimal

This paper presents a theoretical and empirical analysis of two approaches for scaling test-time compute in large language models (LLMs): verifier-based (VB) methods using reinforcement learning or search with verification, and verifier-free (VF) methods that distill or clone search traces. The authors prove that VB methods are superior to VF methods given fixed compute and data budgets, and that the suboptimality of VF methods scales poorly as test-time compute and training data increase, especially when the base LLM has a heterogeneous distribution over correct traces. The theory is corroborated with experiments on didactic and math reasoning problems using 3B, 8B, and 32B parameter pre-trained LLMs. The paper includes code and data via the arXiv submission.

Key Features

Theoretical proof that verifier-based (VB) methods (RL or search with verification) are superior to verifier-free (VF) distillation/cloning
Identifies condition using anti-concentration for heterogeneous correct trace distributions
Shows suboptimality of VF methods scales poorly with test-time compute and data
Empirical validation on math reasoning with 3B, 8B, and 32B LLMs
Open-access paper with code/data links on arXiv

Pros & Cons

Pros
  • Provides rigorous theoretical proof for the superiority of verifier-based methods
  • Empirical results support theory across multiple model sizes
  • Includes code and data for reproducibility
  • Addresses a central debate in LLM test-time compute scaling
  • Open-access publication on arXiv
Cons
  • Not a software tool – it is a research paper, not a ready-to-use application
  • Empirical scope limited to math reasoning tasks; generalizability to other domains not proven
  • Requires substantial compute resources for experiments (3B-32B models)
  • Does not provide an implementation for direct use outside of research replication

Best For

Guiding research on scaling test-time compute for LLMsInforming design of RL and search algorithms for reasoning tasksBenchmarking verifier-based vs. verifier-free approaches in LLM trainingImproving math reasoning performance in large language models

FAQ

What is the main claim of this paper?
The paper proves that verifier-based methods (using RL or search with verification) are far superior to verifier-free distillation/cloning for scaling test-time compute in LLMs, and that the gap widens as compute budget grows.
How was the theory validated?
The theory was validated with experiments on didactic and math reasoning problems using pre-trained LLMs of sizes 3B, 8B, and 32B.
Where can I access the code and data?
Links to code, data, and other resources are provided on the arXiv page under 'Code, Data, Media' and 'Demos' sections.
Is this paper peer-reviewed?
The paper is submitted to arXiv and not peer-reviewed by arXiv itself; it presents original research and is available as a preprint.