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Scaling Test-Time Compute Without Verification or RL is Suboptimal
- The paper presents a theoretical analysis comparing verifier-based (VB) and verifier-free (VF) algorithms for training large language models (LLMs) under varying compute budgets.
- It demonstrates that VB methods outperform VF methods as test-time compute increases, particularly when the base LLM exhibits high heterogeneity and anti-concentration in reward distributions.
- The findings indicate that while both methods can be effective, VB methods scale better with larger budgets, and this gap widens with more prompts for finetuning.
- Empirical results support the theoretical claims, showing that common pre-trained LLMs often meet the necessary conditions for VB advantages
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By Enoch H. Kang- The paper presents a theoretical analysis comparing verifier-based (VB) and verifier-free (VF) algorithms for training large language models (LLMs) under varying compute budgets.
- It demonstrates that VB methods outperform VF methods as test-time compute increases, particularly when the base LLM exhibits high heterogeneity and anti-concentration in reward distributions.
- The findings indicate that while both methods can be effective, VB methods scale better with larger budgets, and this gap widens with more prompts for finetuning.
- Empirical results support the theoretical claims, showing that common pre-trained LLMs often meet the necessary conditions for VB advantages