html,body{height:auto!important;overflow:auto!important} [data-requires-script]{display:none!important} .reader{position:relative!important;overflow:visible!important;min-height:100vh} .reader-frame{height:auto!important;max-height:none!important;margin:auto} .reader-stage,.lesson-reader-stage{position:relative!important;inset:auto!important;overflow:visible!important} .reader-track,.lesson-track{position:relative!important;inset:auto!important;display:block!important;height:auto!important;transform:none!important} .reader-slide,.reader-panel,.lesson-panel{width:100%!important;height:auto!important;min-height:0!important;overflow:visible!important;content-visibility:visible!important} .reader-scroll,.lesson-panel-scroll{position:relative!important;inset:auto!important;height:auto!important;overflow:visible!important;padding-bottom:32px!important} .reader-tabs,.reader-tap,.reader-detail-dock,.card-dock{display:none!important} .lg-screen,.lg-feed,.lg-page,.dr-screen .lg-page{height:auto!important;overflow:visible!important} .lg-page{min-height:100vh} @media(min-width:1000px){.lg-page{min-height:0!important}}

Author

Arnab Bhattacharya

Papers that credit Arnab Bhattacharya in their source bylines. Matching names may refer to different researchers.

1 results

PaperMachine Learning
Language models preserve useful past predictions with FSPO
Across five task accuracy averages, FSPO exceeded on-policy SDPO after five hours on two tested language models.
Added to Chiosa Oct 15, 2026
#cost-performance #policy-distillation #anchor-rehearsal

Arnab Bhattacharya

Language models preserve useful past predictions with FSPO