HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning
arXiv:2601.22448v2 Announce Type: replace-cross Abstract: RLVR has become a standard recipe for training LLMs on reasoning tasks with verifiable outcomes, but when rollout generation dominates the cost, efficiency hinges on which prompts are sampled and when. In practice, prompt pools are often static or only weakly coupled to policy progress, so uniform sampling fails to track the moving capability frontier and wastes rollouts on regions that are already solved or still unreachable. Prior methods improve efficiency via filtering, curricula, adaptive rollout allocation, or teacher guidance, bu
The continuous drive for more efficient and robust LLM training methods necessitates innovations in reinforcement learning techniques to address scalability and performance bottlenecks.
Improved LLM reinforcement learning efficiency directly translates to faster development, lower computational costs, and more capable AI models, impacting numerous downstream applications and sectors.
The proposed 'difficulty-aware heap sampling' and 'on-policy query augmentation' suggest a more adaptive and resource-efficient approach to training LLMs, potentially accelerating progress beyond current static sampling methods.
- · AI compute providers
- · LLM developers
- · Cloud infrastructure providers
- · Inefficient LLM training methodologies
- · Organizations with static model development pipelines
This research could lead to more efficient and powerful LLMs being developed at a lower operational cost.
The cost-effectiveness might democratize access to advanced LLM development, enabling a wider range of players to compete in the AI space.
Increased LLM capabilities and accessibility could accelerate the deployment of sophisticated AI agents across industries, profoundly altering workflows.
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Read at arXiv cs.CL