RLV: Enhancing Language Model Reasoning with Integrated Value-Free Verification

Advancements in Reinforcement Learning for Language Models

Large Language Models (LLMs) have demonstrated impressive reasoning skills by leveraging reinforcement learning (RL) focused on correctness rewards. Recent RL algorithms such as GRPO, VinePPO, and Leave-one-out PPO have improved efficiency by removing the learned value function network and relying instead on empirically estimated returns. This shift significantly reduces computational costs and GPU memory usage, enabling more scalable training for larger models.

The Trade-Off of Removing the Value Function

While eliminating the value function improves efficiency, it also removes a crucial verification mechanism. Traditionally, the value function served as a verifier to assess the correctness of reasoning chains, enhancing inference through parallel search strategies like Best-of-N sampling or weighted majority voting. Without this verifier, LLMs lose an important tool for validating their outputs.

Existing Verification Approaches and Their Limitations

Other verification methods involve test-time classifiers trained via binary classification, preference learning, or next-token prediction. These require substantial additional training data, computational resources, and GPU memory during inference, adding overhead and complexity.

Introducing RLV: Unifying Reasoning and Verification

Researchers from McGill University, Université de Montréal, Microsoft Research, and Google DeepMind proposed RLV, a novel approach that integrates a generative verifier into value-free RL methods without compromising training scalability. RLV exploits the large volumes of data generated during RL training to simultaneously optimize the LLM as both a reasoner and a verifier.

Verification is framed as a next-token prediction task, allowing the same model to generate solutions and provide an intrinsic verification score. Initial experiments show that RLV improves accuracy on the MATH dataset by over 20% compared to baseline RL methods when employing parallel sampling, achieving 8 to 32 times more efficient computation scaling at test time.

Technical Setup and Evaluation

The RLV framework was evaluated using the Hendycks’ MATH dataset, running on 4×A100 80G Nvidia GPUs for 3 hours. Evaluations were performed on MATH500, MATH2, GPQA, and AIME’24 benchmarks. The Qwen2.5 Math 1.5B model was fine-tuned with GRPO, Leave-One-Out PPO, and VinePPO algorithms, both with and without the unified verification mechanism.

Training used a 1024-token context window, with inference generating up to 1024 tokens for MATH500 and 2048 tokens for other datasets.

Key Findings and Performance

RLV demonstrated exceptional scaling in test-time compute, achieving up to 32 times higher efficiency and 4% improved accuracy on MATH500 with 512 samples. Among verification strategies, weighted voting outperformed majority voting and Best-of-N methods when sampling eight or more solutions per problem, effective for both short and long chain-of-thought (CoT) models.

RLV complements sequential inference scaling, with the GRPOV variant attaining the highest success rates on AIME’24 at longer generation lengths. Training the unified verifier requires balancing via the verification coefficient λ, which strongly affects verifier accuracy, improving it from approximately 50% to 80% as λ increases.

Future Directions

The RLV framework establishes a unified approach to reasoning and verification in LLMs without heavy computational overhead. Future work may focus on enhancing the generative verifier to output explicit chain-of-thought explanations, which would necessitate specialized verification-specific CoT datasets or dedicated RL training.

This research paves the way for more reliable and efficient LLM reasoning capabilities by integrating verification directly into value-free reinforcement learning frameworks.

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