Paper

Echo: Learning from Experience Data via User-Driven Refinement

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arXiv:2605. 21984v1 Announce Type: new Abstract: Static "human data" faces inherent limitations: it is expensive to scale and bounded by the knowledge of its creators.

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Authors: Hande Dong, Xiaoyun Liang, Jiarui Yu, Jiayi Lin, Changqing Ai, Feng Liu, Wenjun Zhang, Rongbi Wei, Chaofan Zhu, Linjie Che, Feng Wu, Xin Shen, Dexu Kong, Xiaotian Wang, Qiuyuan Chen, Bingxu An, Yueting Lei, Qiang Lin
Released: 2026-05-23 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Echo: Learning from Experience Data via User-Driven Refinement overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, fill, rect, source, line, rate, completion, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.21984

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