How Transformers Learn to Plan via Multi-Token Prediction
While next-token prediction (NTP) has been the standard objective for training language models, it often struggles to capture global structure in reasoning tasks. Multi-token prediction (MTP) has recently emerged as a promising alternative, yet its underlying mechanisms remain poorly understood. In this paper, we study how MTP facilitates reasoning, with a focus on planning. Empirically, we show that MTP consistently outperforms NTP on both synthetic graph path-finding tasks and more realistic reasoning benchmarks, such as Countdown and boolean satisfiability problems. Theoretically, we analyze a simplified two-layer Transformer on a star graph task. We prove that MTP induces a two-stage ...
| Authors | Jianhao Huang, Zhanpeng Zhou, Renqiu Xia et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 April 2026 |
| Last updated at source | 26 July 2026 |
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