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Debmalya Mandal

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#machine learning Preprint Sep 2026

Local and Global Stability in Performative Reinforcement Learning

This work separates local mixed stability, an occupancy-weighted first-order relaxation that is equivalent to stationarity, from global mixed stability, which certifies against arbitrary deviating policies, and extends both notions to n-player performative Markov games, obtaining local stability with no assumption on t...

Debmalya Mandal · 0 citations
#artificial intelligence Preprint Sep 2026

Inference-Time Nash Alignment

This work forms the problem as obtaining a Nash equilibrium of a two-player zero-sum game between policies, and proposes two algorithms: Best-of-Nash (BoN) and Nash Mirror Descent (NMD), which are proved to achieve a duality gap that matches the problem lower bound.

Hadi Hosseini, Debmalya Mandal, Duo-Han Zhang · 0 citations
Jul 2026

Meta-Learning Preferences for Multilingual LLM Alignment

A meta-learning framework for Reinforcement Learning from Human Feedback and Direct Preference Optimization that learns a transferable initialization that enables effective adaptation to a target language with minimal data.

Jiayin Lin, Seongho Son, Nam Phuong Tran et al. · 0 citations

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