VetEx: Beyond More Experience, Toward Filling Experience Gaps in LLM Agents

Training-free experience reuse typically improves frozen LLM agents through two routes: extracting reusable corrective experience from failed and successful trajectories, or using successful trajectories, directly or after compaction, as few-shot references. However, both routes lack a principled criterion for deciding which experience should actually be admitted for reuse. Reorganizing the receiver’s own failure history alone cannot supply correct behaviors missing from that history; similarly, leaving a successful trajectory unprocessed can preserve misleading actions or unsupported state assumptions, while compaction can remove critical details needed by later decisions. We propose VetEx, which combines corrective experience construction targeting the receiver’s behavioral gaps with reference trajectory cleaning. For corrective experience, VetEx anchors same-task success–failure contrasts on receiver failures and admits donor success trajectories only for tasks the receiver has failed but never succeeded on, filling gaps in positive behavioral evidence. For task references, VetEx cleans retrieved successful trajectories, reducing redundant content while preserving task-relevant action–observation relations, and injects them together with abstract corrective experience into the frozen agent’s initial context. Across ALFWorld, HotpotQA, and WebShop with three frozen receiver models, VetEx outperforms ReAct in all nine settings and achieves a higher three-run mean than the strongest reproduced experience-learning baseline selected by single-run performance in each setting, by an average three-run mean margin of 5.12 points. Controlled analyses show that gains from experience extraction depend on whether newly added evidence fills behavioral gaps in the receiver’s history rather than on experience quantity alone, and that corrective experience and cleaned few-shot trajectories are complementary.