Language Generation as Optimal Control: Closed-Loop Diffusion in Latent Control Space
On the map: Diffusion models → Discrete and text diffusion
Abstract (arXiv)
This work reformulates language generation as a stochastic optimal control problem, providing a unified theoretical perspective to analyze autoregressive and diffusion models and explain their limitations (Efficiency-Fidelity Paradox, Irreversibility Error Propagation, Optimization Tractability and Fidelity) in terms of combination of trajectory singularity, adjoint state vanishing, and gradient absence. To address these issues, we approximate the solution to the Hamilton-Jacobi-Bellman (HJB) equation, yielding an optimal policy that acts as a closed-loop controller. To bypass the intractability of directly solving the HJB PDE, we employ Flow Matching as the optimal trajectory solver within the rectified latent control space. This allows our Manta-LM with Global Integral Operator to approximate the global vector field, effectively realizing a model that simultaneously achieves high-fidelity text generation and efficient, low-cost parallel sampling. Empirically, our method achieves strong performance on language modeling and conditional generation tasks, while exhibiting improved stability, efficiency, and controllability.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 37 of its 48 references that are in the map. First posted on arXiv 14 May 2026.
Builds on
37 of 48 references.
Closest ancestors · 15
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3 more
Other references · 22
- 2020Language Models are Few-Shot Learnerscited by 32,199
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- 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
- 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
- 2016The LAMBADA dataset: Word prediction requiring a broad discourse contextcited by 960
- 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 566
- 2013One Billion Word Benchmark for Measuring Progress in Statistical Language Modelingcited by 302
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- 2020Neural CRF Model for Sentence Alignment in Text Simplificationcited by 106
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- 2024Categorical Flow Matching on Statistical Manifoldscited by 43
- 2023Stochastic optimal transport and Hamilton-Jacobi-Bellman equations on the set of probability measurescited by 1
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