aiqbench analysis
This paper introduces Parallel Inconsistency Guidance (PIG), a technique that employs a lightweight auxiliary model to estimate decoding errors in diffusion language models, thereby improving accuracy without relying on expensive sequential oracles. By dynamically adjusting parallel decode sets based on estimated inconsistency, the method successfully extends the accuracy-throughput frontier across multiple reasoning benchmarks like GSM8K and MATH500. The approach demonstrates that high-performance inference can be achieved with minimal additional parameters while maintaining fidelity to the joint distribution of language.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models
arXiv:2610.17375v1 Announce Type: new Abstract: Diffusion language models accelerate generation by decoding multiple masked positions per forward pass. However, accuracy is known to drop when parallel decoding trajectories deviate from their sequential counterparts, which are slow and expensive "oracles" that adhere to the joint distribution of language. In this paper, we propose Parallel Inconsistency Guidance (PIG), a new paradigm that uses a trained auxiliary model to directly estimate the inconsistency between parallel and sequential generation from a single model forward pass. During inference, masked positions are traversed by decreasing confidence, and tokens are added to the parallel decode set one-at-a-time until the total estimated inconsistency between the parallel and sequential generation exceeds a threshold. Surprisingly, it suffices to train a lightweight ~2M parameter auxiliary model on a small mixture drawn from OpenCoder, GSM8K-train, MATH-train, and MBPP-train. For LLaDA-8B Instruct and NemotronDiffusion-3B Instruct, PIG extends the accuracy-TPF frontier on GSM8K, MATH500, MBPP, HumanEval, and MMLU, when compared to the industry standard Fast-dLLM and measures favorably on LLaDA-8B Instruct against DAPD, KLASS, EB-sampler, and other learned unmasking policies.
Abstract:Diffusion language models accelerate generation by decoding multiple masked positions per forward pass. However, accuracy is known to drop when parallel decoding trajectories deviate from their sequential counterparts, which are slow and expensive "oracles" that adhere to the joint distribution of language. In this paper, we propose Parallel Inconsistency Guidance (PIG), a new paradigm that uses a trained auxiliary model to directly estimate the inconsistency between parallel and sequential generation from a single model forward pass. During inference, masked positions are traversed by decreasing confidence, and tokens are added to the parallel decode set one-at-a-time until the total estimated inconsistency between the parallel and sequential generation exceeds a threshold. Surprisingly, it suffices to train a lightweight ~2M parameter auxiliary model on a small mixture drawn from OpenCoder, GSM8K-train, MATH-train, and MBPP-train. For LLaDA-8B Instruct and NemotronDiffusion-3B Instruct, PIG extends the accuracy-TPF frontier on GSM8K, MATH500, MBPP, HumanEval, and MMLU, when compared to the industry standard Fast-dLLM and measures favorably on LLaDA-8B Instruct against DAPD, KLASS, EB-sampler, and other learned unmasking policies.
66 pages, 22 figures, 11 tables. Code: this https URL
Computation and Language (cs.CL)
arXiv:2610.17375 [cs.CL]
(or arXiv:2610.17375v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.17375 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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