{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/LLMZero","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/5a58620b-56d0-4b3d-b53f-9742e1e42813","json_url":"https://onlylabs.fyi/signals/5a58620b-56d0-4b3d-b53f-9742e1e42813/signal.json","generated_at":"2026-10-25T09:43:09.490Z","evidence_latest_fetched_at":"2026-10-15T07:04:03.06944+00:00","signal_first_seen_at":"2026-10-15T07:00:37.265551+00:00","org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/amazon","dossier_json_url":"https://onlylabs.fyi/labs/amazon/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/5a58620b-56d0-4b3d-b53f-9742e1e42813","signal_json":"https://onlylabs.fyi/signals/5a58620b-56d0-4b3d-b53f-9742e1e42813/signal.json","source":"https://github.com/amazon-science/LLMZero","lab_dossier":"https://onlylabs.fyi/labs/amazon","lab_dossier_json":"https://onlylabs.fyi/labs/amazon/dossier.json","analysis":"https://onlylabs.fyi/analysis/amazon","analysis_json":"https://onlylabs.fyi/analysis/amazon/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/amazon/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Amazon (Nova) published amazon-science/LLMZero (Python). 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A team of LLM agents writes the training code, launches the jobs, reads the live training curves, and keeps searching for a better **training strategy** until the budget runs out. Instead of a human hand-tuning a recipe, LLMZero spends test-time compute to find one. No human in the loop. Official implementation of the EMNLP 2026 (Findings) paper [*LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents*](https://arxiv.org/abs/2606.18388). The tree search is upgraded to **[ExTS](https://github.com/amazon-science/ExTS)** (*Exploit More, Explore Smarter*, EMNLP 2026 Findings, [arXiv:2608.23848](https://arxiv.org/abs/2608.23848)), the official implementation of that paper, which replaces the modified-UCT search used in the LLMZero paper. 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