Length Value Model: Scalable Value Pretraining for Token-Level Length Modeling
Tokens are the fundamental units of computation in modern autoregressive models, and generation length directly influences both inference cost and reasoning performance. Despite its importance, existing approaches model length primarily at the coarse sequence level. We introduce the Length Value Model (LenVM), a token-level framework that estimates the remaining generation length at every decoding step. By formulating length modeling as a value estimation problem and assigning a constant negative reward to each generated token, LenVM predicts a bounded, discounted return that is a monotone proxy for the remaining generation horizon. This value formulation provides annotation-free, dense, ...
| Authors | Zhen Zhang, Changyi Yang, Zijie Xia et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 April 2026 |
| Last updated at source | 20 July 2026 |
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