Auto-research agents, LLM systems that propose, implement, train, and evaluate model changes across iterations, promise to automate applied ML's experimental loop. Over long horizons, execution accuracy is a binding constraint: a change can silently leak held-out data, omit normalization, disconnect a gradient, or leav...
Zheng-Yu Chen, Lin-Feng Liu, Hong Li et al.· 0 citations
On-policy distillation (OPD) trains student agents through teacher supervision on their own interactions with an environment. However, in asynchronous multi-turn training, arrival-order batching can allow a few early or long rollouts to dominate learner updates while other valid rollouts become stale before being used,...
Han-Yang Wang, Ze-Yuan Liu, Zheng-Yu Chen et al.· 0 citations
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