K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling

• URL: http://arxiv.org/abs/2606.10820v2
• Date: Wed, 10 Jun 2026 06:43:22 GMT
• Title: K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling
• Abstract summary: K-Forcing is a push-forward language modeling paradigm for joint next-k-token decoding.<n>We evaluate K-Forcing on LM1B and OpenWebText using a standard causal Transformer backbone.
• Score: 37.46942663162738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token-by-token decoding makes inference memory-bound and inefficient. Existing acceleration approaches, such as speculative decoding and diffusion language models, can yield speedups under certain conditions but do not directly address high-load batch serving--the scenario most critical for industrial-scale deployment. We introduce K-Forcing, a push-forward language modeling paradigm for joint next-k-token decoding. K-Forcing distills an existing AR model into a conditional push-forward mapping--one that transforms independent uniform noise variables into a joint sample of multiple future tokens in a single forward pass. This design preserves fixed-length outputs, reuses the AR teacher backbone, and remains compatible with standard AR serving infrastructure. We train this mapping via progressive self-forcing distillation, which gradually expands the prediction window while enabling the student to closely match the sequence distribution of the AR teacher. We evaluate K-Forcing on LM1B and OpenWebText using a standard causal Transformer backbone. When aggressively configured to generate k = 4 tokens per forward pass, K-Forcing delivers approximately 2.4-3.5x speedup across different batch sizes, while incurring modest quality degradation relative to its AR teacher. As inference increasingly dominates the lifetime compute cost of modern LLMs, K-Forcing offers a promising route toward accelerating AR generation under real-world high-load deployment.

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