← 研踪 PaperTrailarXiv:2611.11565 · v1
Actions Should EARN Their Credit: Test-Time Adaptation and Route Consolidation for Frozen LLM Agents
首次提交 2026-11-09 03:22
版本更新 2026-11-09 03:22
雷达观测 2026-11-10 13:19
摘要
LLM agents can use feedback from repeated attempts to improve their decisions without updating model parameters. However, positive action credits can continue to favor actions that repeatedly achieve the same score, even when higher scores are achievable. Under a fixed interaction budget, trying new actions in the final attempts can also lower performance. We introduce EARN, a method that uses past experience to guide further progress and retain earlier performance through conditional reuse of effective action sequences. EARN reduces action credits for reaching scores repeatedly attained in earlier episodes and assigns larger credits to action segments that reach higher scores. These credits adjust the probabilities of LLM-proposed actions. During the final episodes, EARN directly replays stored actions when the current context matches the recorded context and the actions remain available, bypassing LLM candidate generation. We evaluate performance at the end of the interaction budget using the average score over the final four episodes. Across Jericho, ScienceWorld, and Crafter, EARN achieves the best score on this metric in 31 of 33 task--budget settings among the evaluated methods, including ties. Across the evaluated Jericho budgets, replay bypasses 78.9--86.3\% of candidate-generation calls in the final four episodes, averaged across games.
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v1 · 2026-11-09 03:22 · 最新观测版本
Actions Should EARN Their Credit: Test-Time Adaptation and Route Consolidation for Frozen LLM Agents
LLM agents can use feedback from repeated attempts to improve their decisions without updating model parameters. However, positive action credits can continue to favor actions that repeatedly achieve the same score, even when higher scores are achievable. Under a fixed interaction budget, trying new actions in the final attempts can also lower performance. We introduce EARN, a method that uses past experience to guide further progress and retain earlier performance through conditional reuse of effective action sequences. EARN reduces action credits for reaching scores repeatedly attained in earlier episodes and assigns larger credits to action segments that reach higher scores. These credits adjust the probabilities of LLM-proposed actions. During the final episodes, EARN directly replays stored actions when the current context matches the recorded context and the actions remain available, bypassing LLM candidate generation. We evaluate performance at the end of the interaction budget using the average score over the final four episodes. Across Jericho, ScienceWorld, and Crafter, EARN achieves the best score on this metric in 31 of 33 task--budget settings among the evaluated methods, including ties. Across the evaluated Jericho budgets, replay bypasses 78.9--86.3\% of candidate-generation calls in the final four episodes, averaged across games.
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