Hugging Face Trending Papers
Efficient Reasoning with Flow Language Models
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arXiv AI
Sep 2
Flow Reasoning Models: Turning Flows Into Efficient Recurrent Reasoners
Flow Reasoning Models (FRMs) are a new framework that turns continuous flow models into efficient recurrent reasoners for structured tasks. By self‑conditioning a flow model on its own past outputs, FRMs iteratively refine solutions, allowing parallel decision making and revision. The authors introduce Fixed‑Point Forcing (FPF) to mitigate exposure bias at deeper recursion, and report near‑perfect solve rates on Sudoku‑Extreme, Zebra, and Maze‑Unique, outperforming existing masked‑diffusion and specialized baselines while using far fewer inference FLOPs.
Hugging Face Trending Papers
Oct 7
Unrolled Flow Models for Reasoning
Flow matching enables language generation in few steps, but whether additional integration steps improve reasoning remains unclear. We prove that a flow parameterized by a two-layer Transformer can so...
arXiv Machine Learning
Oct 8
Unrolled Flow Models for Reasoning
arXiv:2610.09759v1 Announce Type: new Abstract: Flow matching enables language generation in few steps, but whether additional integration steps improve reasoning remains unclear. We prove that a flo...
arXiv AI
Oct 2
Hierarchical Continuous Diffusion Language Models
Hierarchical Continuous Diffusion Language Models (HC‑DLM) combine discrete token generation with a continuous latent trajectory in a single denoising process, addressing the independence bottleneck of parallel decoding in discrete diffusion models. The training objective is derived from a variational bound on token likelihood, and the latent state is the sole persistent generative element, with tokens read out and fed back at each step. Experiments on Sudoku, Countdown, and LM1B show HC‑DLM outperforming both discrete and continuous diffusion baselines in puzzle accuracy and generative perplexity.
arXiv Machine Learning
Jun 29
Masked Language Flow Models
arXiv:2606. 27617v1 Announce Type: cross Abstract: Masked Diffusion Models (MDMs) promise fast, parallel language generation, but their reverse transition factorises across token positions -- an approximation that breaks down in the few-step sampling regime where parallel generation ought to provide the greatest efficiency gains.
arXiv Machine Learning
3d ago
Breaking Diffusion: Parallel Reasoning with Draft Refinement
Draft Refinement Language Models (DRLMs) propose parallel revisions to a draft answer using a single denoiser that reconstructs every token from uniformly corrupted drafts, enabling both preservation and repair of tokens. The model evaluates each candidate draft by estimating a cavity energy from forward predictions, selecting revisions that lower this energy without a separate scoring model. Experiments on Countdown, Sudoku-Extreme, and Maze-Hard show DRLM outperforming autoregressive and discrete diffusion baselines, achieving 44.4% on Countdown-5 and 93.9% on Sudoku-Extreme with 6M parameters.