Beyond Mode-Seeking RL: Trajectory-Balance Post-Training for Diffusion Language Models
Diffusion language models are a promising alternative to autoregressive models, but current post-training methods that maximize rewards lead to a failure mode called trajectory locking, where probability mass concentrates on a narrow set of denoising paths, reducing coverage of alternative correct solutions. The proposed TraFL (Trajectory Flow baLancing) uses a trajectory-balance objective to train the policy toward a reward-tilted target distribution anchored to a frozen reference model, with a diffusion-compatible sequence-level surrogate and learned prompt-dependent normalization. TraFL is the only method that improves over the base model in all benchmark settings, with gains that persist as sampling budget increases, and transfers to held-out evaluations.
[2605.13935] Beyond Mode-Seeking RL: Trajectory-Balance Post-Training for Diffusion Language Models
[Submitted on 13 May 2026]
Title:Beyond Mode-Seeking RL: Trajectory-Balance Post-Training for Diffusion Language Models
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Abstract:Diffusion language models are a promising alternative to autoregressive models, yet post-training methods for them largely adapt reward-maximizing objectives. We identify a central failure mode in this setting we call trajectory locking: sampled reward-driven updates over-concentrate probability mass onto a narrow set of denoising paths, reducing coverage of alternative correct solutions under repeated sampling. To address this, we propose TraFL (Trajectory Flow baLancing), a trajectory-balance objective that trains the policy toward a reward-tilted target distribution anchored to a frozen reference model. We make this practical for diffusion language models with a diffusion-compatible sequence-level surrogate and a learned prompt-dependent normalization. Across mathematical reasoning and code generation benchmarks, TraFL is the only evaluated post-training method that improves over the base model in every benchmark-length setting, with gains that persist as the sampling budget increases. The improvements transfer to held-out evaluations: TraFL stays above the base model on Minerva Math and is the strongest method on every LiveCodeBench difficulty split.
Subjects:
Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as: arXiv:2605.13935 [cs.LG]
(or arXiv:2605.13935v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.13935
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Saba Ahmadi [view email] [v1] Wed, 13 May 2026 16:14:46 UTC (107 KB)
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