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DeLoop: Repeated Actions as Self-Supervision for Long-Horizon Agents research paper by Google, 2026

Google · Training and scaling · Oct 14, 2026 · 21 upvotes · unverified

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What it shows

This self-supervised training method teaches AI agents to avoid repetitive, unproductive actions during long tasks by using their own history as feedback.

By Tianle Chen, Pushkar Singh, Ketan Todi and 8 more · arXiv 2610.19376 · PDF · Code

UnverifiedSummarised by a model from the abstract; not yet checked by hand.

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About this paper
Citations
Not counted yet · Semantic Scholar
Upvotes
21 · Hugging Face
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

UpvotesNov 9, 2026Hugging Face upvotes fell from 22 to 21.
UpvotesNov 2, 2026Hugging Face upvotes fell from 33 to 22.
UpvotesOct 26, 2026Hugging Face upvotes rose from 32 to 33.
New summaryOct 26, 2026This self-supervised training method teaches AI agents to avoid repetitive, unproductive actions during long tasks by using their own history as feedback.
TopicOct 26, 2026Moved from Agents and evaluation to Training and scaling.
New paperOct 26, 2026Found by the weekly Hugging Face scan; not yet checked by hand.