TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning
arXiv:2606.11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models. However, rollout-intensive policy optimization is often limited by insufficient reward contrast, arising when overly simple or complex prompts generate low-variance feedback and when outcome-only rewards assign the same terminal assessment to every decision in a multi-turn rollout. Past efforts have focused on allocating available rollout resources to promising prompts, yet they only leverage sampl
The proliferation of large language models and the increasing focus on agentic AI capabilities necessitate more efficient and effective methods for policy optimization and reward allocation in reinforcement learning settings.
Efficient agentic reinforcement learning techniques are critical for developing more capable, autonomous AI systems that can handle complex multi-turn decision-making with limited computational resources.
The proposed TRACE framework offers a unified approach to intelligently allocate rollout budgets, addressing limitations of current methods by improving reward contrast and optimizing resource utilization in agentic systems.
- · AI developers
- · Companies deploying AI agents
- · Reinforcement learning researchers
- · Inefficient AI development pipelines
- · Systems with high computational overheads
More robust and generalizable AI agents will emerge due to improved training efficiency and effectiveness.
Reduced compute costs for developing advanced AI agents could lower barriers to entry for new innovations.
Widespread adoption of highly efficient AI agents could accelerate automation across various industries, impacting white-collar workflows significantly.
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Read at arXiv cs.CL