{ "schema_version": 1, "title": "Repro - Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models", "emoji": "🎯", "space_id": "SabaPivot/repro-finetuning-without-forgetting-icl", "paper": { "arxiv_id": "2602.23197" }, "tags": [ "icml2026-repro", "paper-jl2f2Y3iuC" ], "updated_at": "2026-07-31T04:20:00+00:00", "root": { "slug": "index", "title": "Repro - Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models", "file": "pages/index.md", "children": [ { "slug": "executive-summary", "title": "Executive summary", "file": "pages/executive-summary/page.md", "children": [] }, { "slug": "claim-1-full-fine-tuning-degrades-in-context-learning", "title": "Claim 1: Full fine-tuning degrades in-context learning", "file": "pages/claim-1-full-fine-tuning-degrades-in-context-learning/page.md", "children": [] }, { "slug": "claim-2-value-only-fine-tuning-preserves-in-context-learning", "title": "Claim 2: Value-only fine-tuning preserves in-context learning", "file": "pages/claim-2-value-only-fine-tuning-preserves-in-context-learning/page.md", "children": [] }, { "slug": "claim-3-auxiliary-few-shot-loss-trade-off", "title": "Claim 3: Auxiliary few-shot loss trade-off", "file": "pages/claim-3-auxiliary-few-shot-loss-trade-off/page.md", "children": [] }, { "slug": "claim-4-synthetic-linear-regression-experiment", "title": "Claim 4: Synthetic linear-regression experiment", "file": "pages/claim-4-synthetic-linear-regression-experiment/page.md", "children": [] }, { "slug": "claim-5-qwen2-5-mmlu-experiment", "title": "Claim 5: Qwen2.5 MMLU experiment", "file": "pages/claim-5-qwen2-5-mmlu-experiment/page.md", "children": [] }, { "slug": "conclusion", "title": "Conclusion", "file": "pages/conclusion/page.md", "children": [] } ] }, "agent_view_tokens": 12549, "revision": "1784244535671852170" }