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TRACE is a unified rollout budget allocation framework that enhances reward contrast in multi-turn agentic reinforcement learning by dynamically distributing resources across tree-structured rollouts based on prefix-level informativeness. It improves efficiency and accuracy on agentic benchmarks like Multi-Hop QA.
This paper formulates LLM inference budget allocation as a constrained optimization problem, proposing CLEAR to reallocate resources from low-utility queries to those near emergence thresholds, achieving up to 3× accuracy improvement under tight budgets.
A method that dynamically allocates compute budget to hard problems using Qwen-35B-A3B achieves performance near GPT-5.4-xHigh on the HLE benchmark.