3 citations · 4 across the 6 of their papers we have counts for
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cs.LG2024
Meta-Learning Objectives for Preference Optimization
Carlo Alfano, Silvia Sapora, Jakob Nicolaus Foerster +2
Evaluating preference optimization (PO) algorithms on LLM alignment is a challenging task that presents prohibitive costs, noise, and several variables like model size and hyper-pa…
cs.LG2022★ 1 cited
Linear Convergence for Natural Policy Gradient with Log-linear Policy Parametrization
Carlo Alfano, Patrick Rebeschini
We analyze the convergence rate of the unregularized natural policy gradient algorithm with log-linear policy parametrizations in infinite-horizon discounted Markov decision proces…
cs.LG2021★ 3 cited
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning
Carlo Alfano, Patrick Rebeschini
Cooperative multi-agent reinforcement learning is a decentralized paradigm in sequential decision making where agents distributed over a network iteratively collaborate with neighb…