[2604.18739] Discrete Tilt Matching
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Computer Science > Machine Learning
arXiv:2604.18739 (cs)
[Submitted on 20 Apr 2026 (v1), last revised 19 May 2026 (this version, v3)]
Title:Discrete Tilt Matching
Authors:Yuyuan Chen, Shiyi Wang, Peter Potaptchik, Jaeyeon Kim, Michael S. Albergo<br>View a PDF of the paper titled Discrete Tilt Matching, by Yuyuan Chen and 4 other authors
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Abstract:Masked diffusion large language models (dLLMs) are a promising alternative to autoregressive generation. While reinforcement learning (RL) methods have recently been adapted to dLLM fine-tuning, their objectives typically depend on sequence-level marginal likelihoods, which are intractable for masked diffusion models. To address this, we derive Discrete Tilt Matching (DTM), a likelihood-free method that recasts dLLM fine-tuning as state-level matching of local unmasking posteriors under reward tilting. DTM takes the form of a weighted cross-entropy objective with explicit minimizer, and admits control variates that improve training stability. On a synthetic maze-planning task, we analyze how DTM's annealing schedule and control variates affect training stability and prevent mode collapse. At scale, fine-tuning LLaDA-8B-Instruct with DTM yields strong gains on Sudoku and Countdown while remaining competitive on MATH500 and GSM8K.
Subjects:
Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as:<br>arXiv:2604.18739 [cs.LG]
(or<br>arXiv:2604.18739v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2604.18739
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arXiv-issued DOI via DataCite
Submission history<br>From: Yuyuan Chen [view email]<br>[v1]<br>Mon, 20 Apr 2026 18:43:37 UTC (6,659 KB)
[v2]<br>Sat, 16 May 2026 03:48:01 UTC (6,659 KB)
[v3]<br>Tue, 19 May 2026 01:39:31 UTC (6,659 KB)
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