{ "schema_version": "paper_public_manifest_v1", "paper_id": "mt3-thousand-tasks_2026_08", "slug": "mt3-thousand-tasks", "title": "1,000 Tasks in 1 Day, Thanks to Inductive Bias", "authors": [], "source": { "arxiv_id": "2511.10110", "pdf_url": "https://arxiv.org/pdf/2511.10110", "project_url": "", "github_url": "", "huggingface_url": "", "original_source": "https://arxiv.org/abs/2511.10110" }, "site": { "post_url": "/posts/mt3-thousand-tasks", "canonical_url": "https://haiguangboy.com/posts/mt3-thousand-tasks", "cover_image": "https://static.haiguangboy.com/papers/mt3-thousand-tasks/cover.webp" }, "taxonomy": { "domain": "embodied_ai", "track": "action_generation", "tasks": [ "embodied_ai", "action_generation", "robotics", "latent_state", "Embodied Intelligence", "Imitation Learning", "Data Efficiency" ], "related_topics": [ { "paper_id": "latepost_xuhuazhe_202603_2026_03", "title": "latepost_xuhuazhe_202603", "url": "https://haiguangboy.com/posts/latepost_xuhuazhe_202603", "relation": "contrast", "summary": "Core claim: Inductive bias is an asset in the low-data regime and a liability in the high-data regime—this is a judgment with a crossover point, not a stance-taking, contradicts route bet: the behavior/action part must be a unified model, opposing modular assembly", "strength": "strong" }, { "paper_id": "decompose_and_reorganize_planning_with_primitives_and_visuomotor_policies_learne_2026_08", "title": "Switching Logic Should Not Be Learned by the Policy", "url": "https://haiguangboy.com/posts/dr-lfd", "relation": "contrast", "summary": "Key counter-trend: The decomposition method ramps up quickly at 1–10 demonstrations but plateaus around 50, while monolithic BC accelerates in the 10–50 range, contradicts core claim: Switching logic should be delegated to the planner rather than learned by the policy—this is a structural rebuttal to end-to-end scaling", "strength": "strong" }, { "paper_id": "latent_action_pretraining_through_world_modeling_2026_07", "title": "LAWM: Why Action Labels Become a Burden", "url": "https://haiguangboy.com/posts/latent_action_pretraining_through_world_modeling", "relation": "same_track", "summary": "LAWM: Why Action Labels Become a Burden", "strength": "strong" }, { "paper_id": "sunday_blog_20260717_2026_07", "title": "sunday_blog_20260717", "url": "https://haiguangboy.com/posts/sunday_blog_20260717", "relation": "same_track", "summary": "Key counter-trend: The decomposition method ramps up quickly at 1–10 demonstrations but plateaus around 50, while monolithic BC accelerates in the 10–50 range, validates Pretraining scale converges generalization gap: 82→10→8→4→0pp, out-of-domain 14%→100%", "strength": "strong" }, { "paper_id": "causally_debiased_latent_action_model_for_embodied_action_conditioned_world_mode_2026_07", "title": "World Models Misbehave Because Latent Actions Are Contaminated", "url": "https://haiguangboy.com/posts/cd-lam", "relation": "same_track", "summary": "The ceiling of open-loop replay: Once a trajectory starts, errors cannot be detected or corrected mid-way, validates Two failure modes measured on DreamDojo: zero-action still moves; target-action transfer does not follow", "strength": "strong" }, { "paper_id": "archon_blog_whole_body_intelligence_cn_20260712_2026_07", "title": "Li Hongyang's Whole-Body Intelligence Surpasses GR's Whole-Body Control", "url": "https://haiguangboy.com/posts/archon-whole-body-intelligence", "relation": "same_track", "summary": "Perception dependence: Vision-only, single camera, no tactile, relies on accurate segmentation, validates Four extension dimensions: modality, body, task, failure—parameter count is not among them", "strength": "strong" } ] }, "ruling": { "importance_score": 3.0, "one_sentence": "1,000 Tasks in 1 Day, Thanks to Inductive Bias" }, "asset_base_url": "https://static.haiguangboy.com/papers/mt3-thousand-tasks", "assets": [ { "type": "cover_image", "object_key": "papers/mt3-thousand-tasks/cover.webp", "content_type": "image/webp", "upload_status": "uploaded", "bucket": "paper-assets", "url": "https://static.haiguangboy.com/papers/mt3-thousand-tasks/cover.webp", "role": "post_cover", "size_bytes": 83114 }, { "type": "public_brief", "object_key": "papers/mt3-thousand-tasks/public_brief.md", "content_type": "text/markdown; charset=utf-8", "upload_status": "uploaded", "bucket": "paper-assets", "url": "https://static.haiguangboy.com/papers/mt3-thousand-tasks/public_brief.md", "role": "public_brief", "size_bytes": 5196 }, { "type": "public_manifest", "object_key": "papers/mt3-thousand-tasks/public_manifest.json", "content_type": "application/json; charset=utf-8", "upload_status": "uploaded", "bucket": "paper-assets", "url": "https://static.haiguangboy.com/papers/mt3-thousand-tasks/public_manifest.json", "role": "public_manifest", "size_bytes": 5345 } ], "published_at": "2026-08-05T10:54:28+08:00", "created_at": "2026-08-05T10:54:28+08:00", "updated_at": "2026-09-02T10:55:06+08:00", "analyst_take": { "type": "author_opinion", "text": "The most valuable part is not the 1,000, but the failure directions the authors proactively provide.\n\nThis judgment has a crossover point: The same inductive bias is an asset in the low-data regime and becomes a liability in the high-data regime. Decomposition exploits built-in task structure, so it is strong with small data; precisely because of structural constraints, scaling capacity is limited, retrieval selects only a single demonstration, preventing cross-demonstration knowledge sharing.\n\nThis places it in opposition to both camps. The camp advocating unified models and opposing modular assembly is rebutted by its low-data conclusion; the camp advocating delegating switching logic to the planner is rebutted by its high-data trend. Each side is half right, depending on where you are on the curve.\n\nComparing with another recent work makes this clearer: That work used 200 generated demonstrations to achieve 84% success on a real robot, reporting only gains throughout. This work reports gains, crossover points, failure attributions item by item, and the expectation that it will eventually be surpassed. I discount the credibility of the former; not the latter." } }