# Lingchu Intelligence: Data Strategy Depends on Scenario Complexity Lingchu Intelligence: Data Strategy Depends on Scenario Complexity Full article: https://haiguangboy.com/posts/lingchu-optical ## Core Approach ↳ Three Judgments ① Dual-model division of labor: The policy model Psi-R2 is fed only high-quality successful trajectories, focusing on learning "how to do it right"; the world model Psi-W0 specializes in recording failure samples and edge cases as a "mistake notebook," judging whether actions are reasonable, while also converting human data into robot training data—this division directly opposes the claim that "the action layer must use a unified model" ② Inverted data pyramid: In the pretraining phase, human operation data takes the lead, real-robot data is used only for post-training and deployment adaptation, and internet video is downgraded to a supplementary role. Supporting this judgment are exoskeleton data gloves—5 months, 3 collection sites, several hundred devices, 100,000 hours of multimodal data, with costs compressed to one-tenth of traditional real-robot teleoperation. The plan is to reach 1 million hours within the year, and the first 1,000 hours have been proactively open-sourced ③ Specialize first, then generalize: Rather than spreading across multiple tasks simultaneously, they first rely on the five overlapping conditions of "high unit price/high repetition/low fault tolerance/high precision/manual bottleneck" to make the economics of optical module production lines work. Preliminary estimates show a production loss rate reduction of about 10%, before migrating to adjacent precision manufacturing tasks ## Key Results ## Related Route Comparisons - Opposing route · [D4RT_Efficiently_Reconstructing_Dynamic_Scenes_One](https://haiguangboy.com/posts/d4rt_efficiently_reconstructing_dynamic_scenes_one) `d4rt_efficiently_reconstructing_dynamic_scenes_one_2026_06`: Route bet: Inverted data pyramid—human data as the foundation rather than internet video contradicts research community's bet: feedforward 3D tracking is the key path to 'revitalizing 2D video as 3D data and injecting 3D priors into world models' - Opposing route · [latepost_xuhuazhe_202603](https://haiguangboy.com/posts/latepost_xuhuazhe_202603) `latepost_xuhuazhe_202603_2026_03`: Route bet: Specialize first, then generalize—penetrating a single high-value scenario before migrating to adjacent tasks as a data flywheel contradicts route bet: AI-native triple negation—not robotics/not autonomous driving/not prehistoric deep learning - Same route · [An_Open_Foundation_Model_Towards](https://haiguangboy.com/posts/an_open_foundation_model_towards) `an_open_foundation_model_towards_2026_07`: 'Native human data' pyramid claim: pretraining prioritizes human data, real-robot data only for post-training adaptation validates joint co-training of heterogeneous human-robot data as a structurally suboptimal approach - Same route · [sunday_blog_20260717](https://haiguangboy.com/posts/sunday_blog_20260717) `sunday_blog_20260717_2026_07`: Boundary: all from Lingchu Intelligence's own statements plus media retelling, key figures lack third-party independent verification validates source boundary: self-reported, self-built evaluations, no third party, single task family - Same route · [wx_界面新闻_20260605](https://haiguangboy.com/posts/wx_界面新闻_20260605) `wx_界面新闻_20260605_2026_06`: Boundary: all from Lingchu Intelligence's own statements plus media retelling, key figures lack third-party independent verification validates boundary: robot task completion rates/independent contribution shares not disclosed, more like a data expedition than commercial deployment ## Boundaries · The 10% loss reduction is a "preliminary estimate," and the controlled experiment showing human data comprehensively outperforming teleoperation is "reportedly," with no third-party audit · The timing of topping the MOMO space leaderboard and evaluation details have no independently verified sources · All information comes from Lingchu's official statements plus media retelling, with no third-party replication ## Author's Judgment (Not Paper Content, Cross-Paper Synthesis) The two conflicting edges in the library appear to be about "whose data strategy is more correct," but at the root, it is more likely about differing scenario complexity. Some argue video data is the endgame, facing households—open-ended long-tail scenarios where data strategy must serve generalization. Lingchu faces optical module production lines—fixed processes like inspection, molding, and packaging—where data strategy should serve precision and repetition, not generalization. Scenario dictates strategy; strategy itself is not inherently right or wrong. A question the original text does not answer, yet determines whether the judgment holds: how many distinct scenarios do the 100,000 hours correspond to? If concentrated across several hundred or thousand similar actions, what is bought is high precision and high consistency, not generalization—which is exactly what factories want (yield, cycle time, repetition rate), but it is a different matter from "general capabilities of embodied foundation models." More worth asking: for routine processes like optical module insertion-detection, could traditional robotic arms with vision algorithms achieve comparable performance—if so, the premium "data-driven" buys here may not be as large as the demo suggests. "Specialize first, then generalize" also depends on boundaries: it holds for Lingchu within a closed, limited process set, but directly extending it to refute "must generalize first to be economically viable" may commit the same complexity mismatch—"specializing first" in a closed production line versus an open household tests different things. Whether yield, cycle time, and payback period can be made public after six months is the first hurdle; the "human data vs. video data" divergence may not need a winner, but rather a clear mapping of "which data strategy fits which scenario."