Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language Models
We formulate fixed-budget dLLM batch decoding and introduce Batch-Paced, a training-free method that jointly optimizes the allocation of decoding compute across requests to improve overall batch quality.
ProofPaper ↗
Key points
- As generative language models become more capable and widely used, inference demand grows, making efficient serving increasingly important.
- Discrete diffusion language models (dLLMs) offer a promising path to more efficient inference through parallel token generation.
- Prior dLLM decoding methods, however, focus on improving inference efficiency for individual sequences.
- We further show that Batch-Paced's batch decode time remains competitive and that adding a decoding-depth allowance during Batch-Paced decoding can shorten it further at a modest performance cost.
Sources (1)
- [1]Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 08:08 AM
We formulate fixed-budget dLLM batch decoding and introduce Batch-Paced, a training-free method that jointly optimizes the allocation of decoding compute across requests to improve overall batch quality.
As generative language models become more capable and widely used, inference demand grows, making efficient serving increasingly important.
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