RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning
arXiv:2606.01281v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, its effectiveness is substantially hindered by the prevalence of ineffective training data: many sampled prompts yield response groups that are either entirely correct or entirely incorrect, resulting in zero-variance rewards and limited learning signals. Recent state-of-the-art methods address this issue through extensive LLM rollouts to filter ineffective samples, but at the
The proliferation of LLMs and the increasing demand for advanced reasoning capabilities drive continuous research into optimization techniques to enhance their performance and efficiency.
Improving the efficiency and effectiveness of LLM training, especially in reasoning, is critical for scaling AI applications and reducing computational costs, impacting the economic viability of AI-driven tools.
This research proposes a method to significantly reduce the need for extensive computational rollouts in RLVR for LLMs, making the training process more efficient and potentially leading to faster development cycles for more capable models.
- · AI developers
- · LLM providers
- · Cloud computing providers (through efficiency gains)
- · Companies with inefficient LLM training pipelines
More efficient and capable LLMs for complex reasoning tasks become available sooner.
Accelerated deployment of AI agents and automated systems across various industries due to better reasoning models.
Enhanced competition in the AI market, favoring those who can leverage these optimization techniques for superior product development.
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Read at arXiv cs.LG