BASIS: Batchwise Advantage Estimation from Single-Rollout Information Sharing for LLM Reasoning
arXiv:2605.27293v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has become a standard recipe for improving the reasoning abilities of large language models. Existing algorithms face a tradeoff between computational efficiency and sample efficiency in value estimation and policy learning. We introduce BASIS, a critic-free post-training algorithm designed to address this tradeoff. At each online training step, BASIS samples only one rollout per prompt, but leverages rich information across prompts in the entire batch to improve value function estimation. Our experi
The continuous drive for more efficient and performant Large Language Models (LLMs) requires novel optimization techniques to overcome current computational and sample efficiency bottlenecks, particularly within reinforcement learning paradigms.
Improved efficiency in training LLMs for reasoning could accelerate the development of more capable AI agents, reducing the computational cost and time required to deploy sophisticated AI systems.
BASIS introduces a method to significantly enhance the value estimation in LLM training by sharing information across prompts batch-wise, potentially leading to more robust and sample-efficient learning without the need for critics.
- · AI model developers
- · Cloud computing providers
- · Research institutions
- · Early adopters of advanced AI
- · Companies with inefficient LLM training pipelines
- · Those relying solely on older, less efficient RL algorithms
More efficient and capable LLMs will emerge, able to perform complex reasoning tasks with less training data and computational resources.
The reduced cost of training could democratize access to advanced AI development, accelerating innovation across various sectors.
This could contribute to an overall increase in the quantity and quality of autonomous 'AI Agents', driving new applications and business models.
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