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Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization
Clément Bonet, Pierre-Cyril Aubin-Frankowski, Youssef Mroueh
Optimizing functionals over the space of probability measures is now ubiquitous in machine learning. A widely used approach is to perform the optimization directly over the Wassers…
Guided Speculative Inference for Efficient Test-Time Alignment of LLMs
Jonathan Geuter, Youssef Mroueh, David Alvarez-Melis
We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models. GSI combines soft best-of- test-time scaling with…
GP-MoLFormer-Sim: Test Time Molecular Optimization through Contextual Similarity Guidance
Jiri Navratil, Jarret Ross, Payel Das +4
The ability to design molecules while preserving similarity to a target molecule and/or property is crucial for various applications in drug discovery, chemical design, and biology…
Reinforcement Learning with Verifiable Rewards: GRPO's Effective Loss, Dynamics, and Success Amplification
Youssef Mroueh
Group Relative Policy Optimization (GRPO) was introduced and used recently for promoting reasoning in LLMs under verifiable (binary) rewards. We show that the mean + variance calib…
KL-Regularized RLHF with Multiple Reference Models: Exact Solutions and Sample Complexity
Gholamali Aminian, Amir R. Asadi, Idan Shenfeld +1
Recent methods for aligning large language models (LLMs) with human feedback predominantly rely on a single reference model, which limits diversity, model overfitting, and underuti…
Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training
Youssef Mroueh, Nicolas Dupuis, Brian Belgodere +6
We revisit Group Relative Policy Optimization (GRPO) in both on-policy and off-policy optimization regimes. Our motivation comes from recent work on off-policy Proximal Policy Opti…