arXiv:2605.26606v1 Announce Type: new Abstract: Reinforcement learning (RL) is the dominant paradigm for post-training large language models. However, in the online, on-policy setting, rollout generation dominates the computational cost of training. Group-based policy optimization methods compute advantages from multiple rollouts per prompt, yet they indiscriminately allocate budget to prompts with collapsed reward distributions, wasting expensive rollouts on negligible learning signals. We demonstrate that group-based updates are most effective in regimes of high reward variance. Since the po
The increasing computational cost of training large language models (LLMs) requires optimized resource allocation strategies for continued progress and scalability.
This research directly addresses the dominant computational bottleneck in LLM training, potentially unlocking more efficient development and deployment of advanced AI.
The focus shifts from indiscriminate rollout generation to a more targeted, variance-aware approach for optimizing computational spend in reinforcement learning for LLMs.
- · AI research labs
- · Cloud providers
- · Large Language Model developers
- · Organizations with inefficient RL model training pipelines
More efficient and faster training of large language models will be possible.
This efficiency could accelerate the development of more sophisticated AI agents and applications.
Reduced compute costs could lower the barrier to entry for developing advanced AI, potentially democratizing access to powerful models.
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Read at arXiv cs.LG