{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/expert-upcycling","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/bb1e997e-59bd-4b94-af47-e7b256e8114c","json_url":"https://onlylabs.fyi/signals/bb1e997e-59bd-4b94-af47-e7b256e8114c/signal.json","generated_at":"2026-06-11T02:51:39.970449+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/bb1e997e-59bd-4b94-af47-e7b256e8114c","signal_json":"https://onlylabs.fyi/signals/bb1e997e-59bd-4b94-af47-e7b256e8114c/signal.json","source":"https://github.com/amazon-science/expert-upcycling","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/expert-upcycling (Python). This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. High-signal details: repo amazon-science/expert-upcycling · language Python · Low-stars research repo from Amazon. onlylabs links this event to 1 captured evidence page and 6 related repo signals.","signal_desk":"repos","source_context":{"source_url":"https://github.com/amazon-science/expert-upcycling","source_host":"github.com","occurred_at":"2026-04-15T23:52:22+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source","context":"Python"},"context_markers":[{"label":"Lab","value":"Amazon (Nova)","source":"signal"},{"label":"Signal desk","value":"repos","source":"signal"},{"label":"Source host","value":"github.com","source":"source"},{"label":"Repository","value":"amazon-science/expert-upcycling","source":"source"},{"label":"Language","value":"Python","source":"source"},{"label":"Stars","value":"14","source":"traction"},{"label":"Notability","value":"Low-stars research repo from Amazon","source":"signal"},{"label":"Watch term","value":"Eval 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new repository signals into early evidence of tooling, eval, infrastructure, model-adjacent, or product work before it appears in polished launch channels.","evidence_focus":["repo name","owner","description","language","stars","source URL","first seen time","data, eval, infra, safety, and product terms"],"extraction_questions":["What technical area does this repository expose?","Does the repo imply eval, data, infrastructure, agent, or deployment work?","Is the repo new evidence for a lab direction that is not yet in writing or releases?","Which related signals should an analyst inspect next?"],"signal_questions":["What does this new repository reveal before a formal announcement exists?","What technical area does this repository expose?","Does the repo imply eval, data, infrastructure, agent, or deployment work?","Do the 6 related repo signals show a repeated pattern?"],"output_fields":["org","repo","technical_theme","data_business_lane","evidence_url"],"data_business_relevance":"New 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analyze Amazon (Nova)'s repo signal \"amazon-science/expert-upcycling\" for frontier lab strategy."},"semantic_triples":[{"subject":"Amazon (Nova)","predicate":"published repo","object":"amazon-science/expert-upcycling","text":"Amazon (Nova) published repo amazon-science/expert-upcycling."},{"subject":"amazon-science/expert-upcycling","predicate":"is classified as","object":"repo signal","text":"amazon-science/expert-upcycling is classified as repo signal."},{"subject":"amazon-science/expert-upcycling","predicate":"belongs to","object":"repos desk","text":"amazon-science/expert-upcycling belongs to repos desk."},{"subject":"amazon-science/expert-upcycling","predicate":"has context","object":"Python","text":"amazon-science/expert-upcycling has context Python."},{"subject":"amazon-science/expert-upcycling","predicate":"has evidence coverage","object":"1 captured evidence page","text":"amazon-science/expert-upcycling has evidence coverage 1 captured evidence 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This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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Scaling laws show that MoE quality improves predictably with total expert count at fixed active computation, but training large MoEs from scratch is expensive — memory, gradients, and all-to-all communication all scale with total parameters. Expert upcycling sidesteps this by starting training with a smaller E-expert model and expanding to mE experts mid-training via the upcycling operator: 1. **Expert replication** — each expert is duplicated (high-utility experts receive more copies via gradient-based importance scores). 2. **Router extension** — router weights are copied to new slots with small bias perturbations to seed routing diversity. 3. **Continued pre-training (CPT)** — stochastic gradient diversity and loss-free load balancing break symmetry among..."},"evidence_pages":[{"url":"https://github.com/amazon-science/expert-upcycling","final_url":"https://github.com/amazon-science/expert-upcycling","title":"amazon-science/expert-upcycling repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T02:51:39.970449+00:00","bytes":23132,"raw_path":"672ce1ce817c1f27abf6ef1dc7f89e3a75ba0edc8d0809493cd95937e610f63a.json","content_hash":"08049d389d761f7c4007d0b7ee8398c14d216a4820397c633d924eef4ad622c0","excerpt_chars":1200,"truncated":true,"excerpt":"amazon-science/expert-upcycling Language: Python License: NOASSERTION Stars: 14 Forks: 2 Open issues: 0 Created: 2026-04-15T23:52:22Z Pushed: 2026-04-15T23:58:50Z Default branch: main Fork: no Archived: yes README: Expert Upcycling **Capacity expansion for Mixture-of-Experts models during continued pre-training.** > Dwivedi et al., *\"Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts\"* (preprint). Scaling laws show that MoE quality improves predictably with total expert count at fixed active computation, but training large MoEs from scratch is expensive — memory, gradients, and all-to-all communication all scale with total parameters. Expert upcycling sidesteps this by starting training with a smaller E-expert model and expanding to mE experts mid-training via the upcycling operator: 1. **Expert replication** — each expert is duplicated (high-utility experts receive more copies via gradient-based importance scores). 2. **Router extension** — router weights are copied to new slots with small bias perturbations to seed routing diversity. 3. **Continued pre-training (CPT)** — stochastic gradient diversity and loss-free load balancing break symmetry among..."}],"related_signals":[{"id":"087c32a2-6ad0-4981-9315-11fdd32a0153","url":"https://onlylabs.fyi/signals/087c32a2-6ad0-4981-9315-11fdd32a0153","source_url":"https://github.com/amazon-science/reskill","title":"amazon-science/reskill","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-06-04T02:13:35+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e5701aed-6cd3-48dd-bfa6-ef839031e2e8","url":"https://onlylabs.fyi/signals/e5701aed-6cd3-48dd-bfa6-ef839031e2e8","source_url":"https://github.com/amazon-science/dualkv-flash-attn-for-rl","title":"amazon-science/dualkv-flash-attn-for-rl","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-27T17:38:58+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"8af28f0c-7331-4b08-b517-e18b3555e503","url":"https://onlylabs.fyi/signals/8af28f0c-7331-4b08-b517-e18b3555e503","source_url":"https://github.com/amazon-science/EvoMAS","title":"amazon-science/EvoMAS","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-19T19:23:29+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e3ff8718-7daa-4ebd-a3e6-3d825c538b74","url":"https://onlylabs.fyi/signals/e3ff8718-7daa-4ebd-a3e6-3d825c538b74","source_url":"https://github.com/amazon-science/adaptive-layerwise-perturbation","title":"amazon-science/adaptive-layerwise-perturbation","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-14T17:44:17+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"9afcd328-0124-485c-8ace-9c3ad546e316","url":"https://onlylabs.fyi/signals/9afcd328-0124-485c-8ace-9c3ad546e316","source_url":"https://github.com/amazon-science/temporal-reasoning-dataset","title":"amazon-science/temporal-reasoning-dataset","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-13T13:07:08+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e19ce80b-3d6a-4aaf-9b1a-82d1b19ab682","url":"https://onlylabs.fyi/signals/e19ce80b-3d6a-4aaf-9b1a-82d1b19ab682","source_url":"https://github.com/amazon-science/PROF-GRPO","title":"amazon-science/PROF-GRPO","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-12T19:43:55+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"}]}