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Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages

arXiv:2603.12554v1 Announce Type: cross Abstract: Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging due to intractable sequence-level likelihoods. Existing approaches therefore rely on surrogate likelihoods or heuristic approximations, which can introduce bias and obscure the sequential structure of denoising. We formulate diffusion-based sequence generation as a finite-horizon Markov decision process over the denoising trajectory and derive an exact, unbiased policy gradient that decomposes over denoising steps and is expressed in terms of intermediate advantages, without requiring explicit evaluation of the sequence likelihood. To obtain a practical and compute-efficient estimator, we (i) select denoising steps for policy updates via an entropy-guided approximation bound, and (ii) estimate intermediate advantages using a one-step denoising reward natural

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Vishnu Teja Kunde, Fatemeh Doudi, Mahdi Farahbakhsh, Dileep Kalathil, Krishna Narayanan, Jean-Francois Chamberland · · 1 min read · 8 views
#cs.LG #cs.AI #cs.CL

arXiv:2603.12554v1 Announce Type: cross Abstract: Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging due to intractable sequence-level likelihoods. Existing approaches therefore rely on surrogate likelihoods or heuristic approximations, which can introduce bias and obscure the sequential structure of denoising. We formulate diffusion-based sequence generation as a finite-horizon Markov decision process over the denoising trajectory and derive an exact, unbiased policy gradient that decomposes over denoising steps and is expressed in terms of intermediate advantages, without requiring explicit evaluation of the sequence likelihood. To obtain a practical and compute-efficient estimator, we (i) select denoising steps for policy updates via an entropy-guided approximation bound, and (ii) estimate intermediate advantages using a one-step denoising reward naturally provided by the diffusion model, avoiding costly multi-step rollouts. Experiments on coding and logical reasoning benchmarks demonstrate state-of-the-art results, with strong competitive performance on mathematical reasoning, outperforming existing RL post-training approaches for DLMs. Code is available at https://github.com/vishnutez/egspo-dllm-rl.

Executive Summary

This article presents a novel reinforcement learning approach for post-training diffusion language models (DLMs), addressing the challenges of intractable sequence-level likelihoods and heuristic approximations. By formulating diffusion-based sequence generation as a finite-horizon Markov decision process, the authors derive an exact, unbiased policy gradient that decomposes over denoising steps. The approach selects denoising steps via entropy-guided approximation bound and estimates intermediate advantages using a one-step denoising reward. Experiments demonstrate state-of-the-art results on coding and logical reasoning benchmarks, with strong competitive performance on mathematical reasoning. This advancement has significant implications for the development of more efficient and effective language models.

Key Points

  • ▸ Formulates diffusion-based sequence generation as a finite-horizon Markov decision process
  • ▸ Derives an exact, unbiased policy gradient that decomposes over denoising steps
  • ▸ Selects denoising steps via entropy-guided approximation bound
  • ▸ Estimates intermediate advantages using a one-step denoising reward

Merits

Strength

The proposed approach addresses the challenges of intractable sequence-level likelihoods and heuristic approximations in post-training DLMs.

Robustness

The method demonstrates strong competitive performance on mathematical reasoning, outperforming existing RL post-training approaches for DLMs.

Efficiency

The approach selects denoising steps via entropy-guided approximation bound, which is computationally efficient and practical.

Demerits

Limitation

The approach relies on a specific diffusion model, which may not be applicable to all types of language models.

Expert Commentary

The article presents a novel and promising approach to reinforcement learning for post-training DLMs. The authors' formulation of diffusion-based sequence generation as a finite-horizon Markov decision process is a significant innovation, as it enables the derivation of an exact, unbiased policy gradient. The selection of denoising steps via entropy-guided approximation bound and the estimation of intermediate advantages using a one-step denoising reward are also noteworthy contributions. The experiments demonstrate the effectiveness of the approach on coding and logical reasoning benchmarks, and its competitive performance on mathematical reasoning. However, the approach's reliance on a specific diffusion model may limit its applicability to other types of language models.

Recommendations

  • ✓ Future research should explore the extension of the proposed approach to other types of language models.
  • ✓ The development of more accurate and unbiased evaluation metrics for language models is essential to fully leverage the potential of this approach.

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