Listwise Policy Optimization: Group-based RLVR as Target-Projection on the LLM Response Simplex
arXiv:2605.06139v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard approach for large language models (LLMs) post-training to incentivize reasoning capacity. Among existing recipes, group-based policy gradient is prevalent, which samples a group of responses per prompt and updates the policy via group-relative advantage signals. This work reveals that these optimization strategies share a common geometric structure: each implicitly defines a target distribution on the response simplex and projects toward it via first-order approximat
This work is published as LLM post-training techniques, particularly those focusing on reasoning capacity like RLVR, are rapidly evolving and becoming central to advanced AI development.
Improving the verifiability and reasoning capacities of LLMs through novel optimization strategies directly impacts their reliability and applicability across critical domains.
New optimization strategies for LLMs become more efficient and geometrically understood, potentially accelerating the development of more capable and trustworthy AI models.
- · AI researchers
- · LLM developers
- · Companies deploying AI agents
- · AI infrastructure providers
- · Developers relying on less efficient RL techniques
- · Companies with suboptimal LLM training pipelines
More robust and reliable LLMs with enhanced reasoning capabilities become available sooner.
Accelerated deployment of autonomous AI agents and complex AI applications across various industries due to improved LLM performance.
Increased competition among foundation model providers as optimization breakthroughs become accessible, potentially leading to more commoditized advanced AI.
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Read at arXiv cs.LG