Apple Proposes Token-Level Length Value Model to Optimize Inference Cost
Apple ML Research · rss · 2026-07-20
Apple's machine learning team introduced the Length Value Model (LenVM), a novel framework designed to address how generation length impacts both inference cost and reasoning performance in autoregressive models.
While existing approaches primarily operate at a coarse-grained sequence level, LenVM introduces fine-grained, token-level length modeling. By formulating length modeling as a value estimation problem, the framework predicts the remaining generation length at each decoding step and assigns a constant negative reward to each generated token, enabling more scalable and precise pretraining.
More from Research
- Causal-only attention for non-generative tasks is wasteful, argues HF engineer — antoine_chaffin · 2026-09-11
- Catholic University of Chile researcher: scaling AI feedback is key to sustainable medical education — julianvarascom · 2026-09-11
- Nature paper images cellular activity across all organs, revealing body-wide circuits — arjunrajlab · 2026-09-11
- SignNet 1M Dataset Released for Sign Language Research — ducha_aiki · 2026-09-11
- ECCV26 Oral: Flow Matching Enables Single-Stage Multi-View Point Cloud Registration — ducha_aiki · 2026-09-11
- InFlux++ Method Released — ducha_aiki · 2026-09-11