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
- Jeff Heaton's Intro to the Math of Neural Networks eBook Is Free to Download — blaizedsouza · 2026-09-11
- Mathematician Daniel Litt Launches Problem Repo to Track Human vs AI Progress: 15 Problems, 1 Solved — littmath · 2026-09-11
- Open ECDSA.fail challenge uses AI agents to shrink Shor's-algorithm quantum circuits for Bitcoin keys — StefanoGogioso · 2026-09-11
- Alex Townsend posts 200 open problems in numerical linear algebra for humans and AI agents — IgorCarron · 2026-09-11
- Navier-Stokes, Riemann, P vs NP: what this week's math buzzwords mean for you — koltregaskes · 2026-09-11
- Fruit fly brain as an LLM: connectome-driven language model demo goes live — ngxson · 2026-09-11