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Table 9.Word clouds of sampled tokens from s1K within each frequency bin, alongside the average LLaDA confidence computed overall tokens in that bin

Again, we observe a clear frequency–confidence trend: high-frequency tokens are associated with higher average confidence, while rare tokens tend to receive lower confidence, consistent with the patterns in our aggregate plots · 2025

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Learnability-Informed Fine-Tuning of Diffusion Language Models

cs.CL · 2026-05-21 · unverdicted · novelty 7.0

LIFT is a learnability-informed SFT algorithm for diffusion LMs that aligns token difficulty with diffusion time steps, yielding up to 3x gains on AIME'24 and AIME'25 over standard SFT baselines.

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  • Learnability-Informed Fine-Tuning of Diffusion Language Models cs.CL · 2026-05-21 · unverdicted · none · ref 18

    LIFT is a learnability-informed SFT algorithm for diffusion LMs that aligns token difficulty with diffusion time steps, yielding up to 3x gains on AIME'24 and AIME'25 over standard SFT baselines.