Publications

On Reparameterizing the Score Function
Mohammad Hossein Amani , Leonardo Bocchieri , Maxime Peyrard , Clément Hongler , Robert West , NeurIPS, 2026.

ArXiv

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We introduce History-Reparameterized (HR) policy gradients, which let gradients of downstream log-probabilities flow backward through a relaxed, differentiable history of the generated sequence, easing credit assignment under sparse rewards. We derive Optimal HR (OHR), a control-variate estimator with a closed-form coefficient that minimizes gradient variance.

RL for Reasoning by Adaptively Revealing Rationales
Mohammad Hossein Amani , Aryo Lotfi , Nicolas Mario Baldwin , Samy Bengio , Mehrdad Farajtabar , Emmanuel Abbe , Robert West , ICLR, 2026.

ArXiv | Code | ICLR'26

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We proposed an adaptive curriculum-based RL algorithm that reveals partial rationales based on per-sample learning estimation of the value of a state. We demonstrate — both empirically and theoretically — a separation result: on complex reasoning tasks, neither SFT nor vanilla RL generalizes with increasing complexity, while AdaBack enables gradual abstraction learning — without ever fine-tuning on full demonstrations.

Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Apertus Team , ACL, 2026.

ArXiv | ACL'26

Symbolic Autoencoding with Straight-Through Gradient Estimators
Mohammad Hossein Amani , Nicolas Mario Baldwin , Amin Mansouri , Martin Josifoski , Maxime Peyrard , Robert West , 2024.

ArXiv | Code | Poster | ICML'24 Workshop

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In this project, we used straight-through estimators to optimize over a discrete latent space of sentences—effectively treating language itself as an optimizable hidden variable.

Sharp asymptotics on the compression of two-layer neural networks
Mohammad Hossein Amani , Simone Bombari , Rattana Pukdee , Stefano Rini , Marco Mondelli , 2022.

ArXiv | ITW'22

Memorization and optimization in deep neural networks with minimum over-parameterization
Simone Bombari , Mohammad Hossein Amani , Marco Mondelli , 2022.

ArXiv | Neurips'22