Multi-token prediction paper shows how Transformers can learn planning by reverse reasoning
CatAstro_Piyush · x · 2026-07-25
Accepted at COLM 2026
The paper “How Transformers Learn to Plan via Multi-Token Prediction” has been accepted to COLM 2026.
- The authors argue that Multi-Token Prediction (MTP) improves planning because it induces gradient decoupling.
- This, in turn, enables reverse reasoning: the model first looks at the goal and then traces the path back.
- The attached heatmaps contrast NTP and MTP, showing a different internal attention/weight pattern consistent with the proposed mechanism.
More from Research
- ICML 2026 oral paper replication scores stay middling after a stricter re-scoring — profjamesevans · 2026-07-27
- Long-running agents will need immutable event logs, this thread argues — sebpaquet · 2026-07-27
- Seed IQ navigates Doom II, prompting questions about benchmarks beyond ARC-AGI — Fit_Transition8824 · 2026-07-27
- Agentic Data Science in Practice: Agents Write Code but Answer Wrong Questions — hugobowne · 2026-07-27
- A concise canon of foundational papers in ML, systems, NLP, speech, and audio — deliprao · 2026-07-27
- TechCrunch says brain-wave signals could be the next unlock for physical AI training — TechCrunch AI · 2026-07-27