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🤯 big update to our flow map language models paper!
we believe this is the future of non-autoregressive text generation.
read about it in the blog: one-step-lm.github.io/blog/
full details in the paper: arxiv.org/abs/2602.16813
we introduce a new class of continuous flow-based language models and distill them into their corresponding flow map for one-step text generation.
we beat all discrete diffusion baselines at ~8x speed!
v2 gives a complete theory of the flow map over discrete data, with three equivalent ways to learn it (semigroup, lagrangian, eulerian). it turns out you can train these with cross-entropy objectives that look very similar to standard discrete diffusion — but without the factorization error that kills discrete methods at few steps.
beyond improving results across the board, we showcase properties that are unique to continuous flows. in particular, inference-time steering and guidance become straightforward. autoguidance brings generative perplexity down to 51.6 on LM1B, while discrete baselines completely collapse at the same guidance scale.
we also show reward-guided generation for steering topic, sentiment, grammaticality, and safety at inference time — and it works even at 1-2 steps with our flow map model. simple, well-understood techniques from continuous flows just work incredibly well in practice for language.
we’re extremely excited about the future of this class of models.
stay tuned for results on scaling, reasoning, and reinforcement learning-based fine-tuning. 🚀
we believe this is the future of non-autoregressive text generation.
read about it in the blog: one-step-lm.github.io/blog/
full details in the paper: arxiv.org/abs/2602.16813
we introduce a new class of continuous flow-based language models and distill them into their corresponding flow map for one-step text generation.
we beat all discrete diffusion baselines at ~8x speed!
v2 gives a complete theory of the flow map over discrete data, with three equivalent ways to learn it (semigroup, lagrangian, eulerian). it turns out you can train these with cross-entropy objectives that look very similar to standard discrete diffusion — but without the factorization error that kills discrete methods at few steps.
beyond improving results across the board, we showcase properties that are unique to continuous flows. in particular, inference-time steering and guidance become straightforward. autoguidance brings generative perplexity down to 51.6 on LM1B, while discrete baselines completely collapse at the same guidance scale.
we also show reward-guided generation for steering topic, sentiment, grammaticality, and safety at inference time — and it works even at 1-2 steps with our flow map model. simple, well-understood techniques from continuous flows just work incredibly well in practice for language.
we’re extremely excited about the future of this class of models.
stay tuned for results on scaling, reasoning, and reinforcement learning-based fine-tuning. 🚀
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