A novel framework, TRACE, aims to significantly bolster the efficiency of reinforcement learning with verifiable rewards (RLVR) for agentic large language models (LLMs). Current RLVR implementations frequently suffer from "insufficient reward contrast," a critical impediment to effective policy optimization. This issue arises from two primary sources: prompts that are either too simplistic or overly complex, yielding low-variance feedback, and outcome-only rewards that indiscriminately assign the same terminal assessment across diverse behaviors. TRACE, a unified rollout budget allocation framework, directly confronts this by optimizing how computational resources are expended during the learning process. By strategically distributing the "rollout" budget, TRACE ensures LLMs receive more discriminative and meaningful reward signals, thereby enhancing their reasoning and autonomous agentic behaviors1. This development, published on arXiv on June 9, 2026, promises to unlock more sophisticated LLM capabilities. For security practitioners, advancements in agentic LLMs necessitate a proactive re-evaluation of trust boundaries and potential exploitation vectors, as improved autonomy directly correlates with evolving risk profiles.