A single-parameter Tsallis loss continuum unifies SFT and RLVR, derives time-to-escape bounds for cold start, and yields GARL and PAFT estimators that improve performance on QA reasoning tasks.
Each gm marginalizes out the output y given z(m) analytically via wm =p θ(y∗ |x ∗,z (m)), rather than relying on a sampled output and binary reward
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How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum
A single-parameter Tsallis loss continuum unifies SFT and RLVR, derives time-to-escape bounds for cold start, and yields GARL and PAFT estimators that improve performance on QA reasoning tasks.