BV-Blend: Uncertainty-Weighted Historical Baselines for Stable Critic-Free RL with Verifiable Rewards
arXiv:2606.28707v1 Announce Type: new Abstract: Critic-free reinforcement learning with verifiable rewards (RLVR), exemplified by Group Relative Policy Optimization (GRPO), avoids training a value function (critic) and reduces memory and compute overhead relative to critic-based PPO pipelines for aligning large language models. However, GRPO-style advantage estimation depends on prompt-local (within-prompt-group) reward statistics and can be unstable. In particular, when all rollouts in a prompt group receive identical rewards, the within-group reward variance becomes zero, and group normaliza
The continuous drive to optimize and scale large language models necessitates novel approaches to reinforcement learning, particularly those that address computational and stability challenges.
Improving the stability and efficiency of critic-free reinforcement learning is crucial for the faster, more resource-effective alignment of large language models.
New methods like BV-Blend aim to overcome the instability of existing critic-free RL techniques, potentially lowering the computational barrier for advanced AI development.
- · AI researchers
- · Large Language Model developers
- · Cloud computing providers
- · High-compute RL techniques
More stable and efficient training of large language models without reliance on complex critic networks.
Reduced computational overhead could democratize advanced AI development, allowing more actors to fine-tune and align sophisticated models.
Accelerated deployment of nuanced and contextually aware AI agents due to improved alignment stability and speed, impacting various industries.
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Read at arXiv cs.AI