Efficient Reasoning with Flow Language Models

Key points

• We investigate this question by comparing the reasoning efficiency of FLMs and discrete diffusion models, measured by solution accuracy under matched denoising steps.1
• Unlike discrete diffusion, which passes categorical states between denoising steps, FLMs evolve a continuous sequence representation throughout denoising and decodes it into discrete tokens only at the end.1
• Our theoretical analysis shows, from a superposition perspective, how information retained in these continuous states can benefit reasoning.1
• Furthermore, our experiments on maze planning and Sudoku tasks show that FLMs achieve greater reasoning efficiency in the few-step regime: FLMs achieves higher sequence accuracy than discrete diffusion baselines at matched model sizes and small denoising steps.1

Sources (1)

• [1]Efficient Reasoning with Flow Language Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:18 AM

  Flow Language Models (FLMs) have emerged as a continuous-state alternative to discrete diffusion language models, yet the role of their continuous representations in reasoning remains unclear.

  We investigate this question by comparing the reasoning efficiency of FLMs and discrete diffusion models, measured by solution accuracy under matched denoising steps.

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