Training Models to Withstand Long-Term Error Accumulation
HeyNayeem · x · 2026-07-13
The author believes the key contribution is not the demo quality, but that stability comes from training.
- The focus is on reducing long-horizon error accumulation and autoregressive drift.
- Their goal can be summarized as: Train the model to survive its own mistakes.
- That is, the system must tolerate error amplification over long rollouts, not just look good on short clips.
Related event: LingBot-World 2.0 Released: Focusing on Hour-Long Stable Interaction(13 posts)→
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