Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM
  Reasoners With Verifiers
Prevalent reinforcement learning~(RL) methods for fine-tuning LLM reasoners,
such as GRPO or Leave-one-out PPO, abandon the learned value function in favor
of empirically estimated returns. This hinders test-time compute scaling that
relies on using the value-function for verification. In this work, we propose
RL^V that augments any ``value-free'' RL method by jointly training the LLM
as both a reasoner and a generative verifier using RL-generated data, adding
verification capabilities without significant overhead. Empirically, RL^V
boosts MATH accuracy by over 20\% with parallel sampling and enables
8-32times efficient test-time compute scaling compared to the base RL
method. RL^V also exhibits strong generalization capabilities for both
easy-to-hard and out-of-domain tasks. Furthermore, RL^V achieves
1.2-1.6times higher performance when jointly scaling parallel and sequential
test-time compute with a long reasoning R1 model.
681e4de2f2cba17ad23d9f08
Kusha Sareen
claimed_verified
2025-05-12T06:50:51.576Z
6684206a55fa08351af543f6
/avatars/9f706cf4c1f6ea4899a4d951758834ad.svg
Kusha Sareen
user
kushasareen
681e4de2f2cba17ad23d9f09
Morgane M Moss
681e4de2f2cba17ad23d9f0a
Alessandro Sordoni
681e4de2f2cba17ad23d9f0b
Rishabh Agarwal
681e4de2f2cba17ad23d9f0c
Arian Hosseini
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM
  Reasoners With Verifiers
Prevalent reinforcement learning~(RL) methods for fine-tuning LLM reasoners,
such as GRPO or Leave-one-out PPO, abandon the learned value function in favor
of empirically estimated returns. This hinders test-time compute scaling that
relies on using the value-function for verification. In this work, we propose
RL^V that augments any ``value-free'' RL method by jointly training the LLM
as both a reasoner and a generative verifier using RL-generated data, adding
verification capabilities without significant overhead. Empirically, RL^V
boosts MATH accuracy by over 20\% with parallel sampling and enables
8-32times efficient test-time compute scaling compared to the base RL
method. RL^V also exhibits strong generalization capabilities for both
easy-to-hard and out-of-domain tasks. Furthermore, RL^V achieves
1.2-1.6times higher performance when jointly scaling parallel and sequential
test-time compute with a long reasoning R1 model.
RL$^V$ enhances LLM reasoners by integrating verification capabilities, improving MATH accuracy and enabling efficient test-time compute scaling.
reinforcement learning RL GRPO Leave-one-out PPO value function generative verifier RL-generated data MATH accuracy parallel sampling test-time compute scaling generalization capabilities long reasoning model