ArXiv

Posterior Refinement: Fast Language Generation via Any-Order Flow Maps

2026-06-23 · 16:33 UTC ·Manan Agarwal, Sheel Shah, Chanhyuk Lee... ·1 min read
Authors
Manan Agarwal, Sheel Shah, Chanhyuk Lee...
Categories
cs.CL
arXiv
https://arxiv.org/abs/2606.24773v1
PDF
https://arxiv.org/pdf/2606.24773v1

Brief

Posterior Refinement: Fast Language Generation via Any-Order Flow Maps (Agarwal et al., 2026) introduces FMLM+, which combines Flow Map LMs with masking-style noise schedules to produce single-step full-sequence outputs while scoring token-level global consistency. The paper presents Posterior Refinement, an adaptive inference refinement that enables self-correction and matches discrete-baseline quality with 32x fewer NFEs, improving speed–quality tradeoffs versus MDMs and prior FMLMs.

Why it matters

FMLM+ augments Flow Map Language Models with masking-style noise schedules to generate full sequences in a single step while simultaneously scoring each token's global consistency a posteriori.

Key details

  • Posterior Refinement is an inference-time, adaptive self-correction strategy that matches the performance of discrete baselines using 32x fewer neural function evaluations (NFEs).
  • On diverse benchmarks, FMLM+ with Posterior Refinement improves the speed–quality tradeoff compared to both Masked Diffusion Models (MDMs) and original FMLMs.
Source evidence

Abstract

Non-autoregressive generation offers a powerful paradigm for iterative refinement, allowing models to recursively critique, erase and regenerate arbitrary subsets of tokens. However, existing non-autoregressive models fail to realize this potential. Masked Diffusion Models (MDMs) suffer from factorization error, causing sample quality to collapse when generating multiple tokens simultaneously. Flow Map Language Models (FMLMs) circumvent this bottleneck via joint sequence transport for excellent few-step generation, but sacrifice the inference-time flexibility of MDMs. We introduce FMLM+, a framework that bridges this gap by equipping FMLM with masking-style noise schedules. While generating the full sequence in a single step, FMLM+ simultaneously scores the global consistency of each token a posteriori. We leverage this to introduce Posterior Refinement, a novel inference-time refinement strategy that enables the model to adaptively self-correct its outputs, matching the performance of discrete baselines with 32x fewer NFEs. Across diverse benchmarks, we demonstrate that FMLM+ with Posterior Refinement improves the speed--quality tradeoff over both MDM and FMLM families, providing a scalable foundation for high-fidelity language modeling.

Comment: 24 pages, 23 figures