Poster
A General Theoretical Paradigm to Understand Learning from Human Preferences
Mohammad Gheshlaghi Azar · Zhaohan Daniel Guo · Bilal Piot · Remi Munos · Mark Rowland · Michal Valko · Daniele Calandriello
MR1 & MR2 - Number 56
Abstract:
The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. The second assumes that a reward model trained on these pointwise rewards can generalize from collected data to out-of-distribution data sampled by the policy. Recently, Direct Preference Optimisation DPO has been proposed as an approach that bypasses the second approximation and learn directly a policy from collected data without the reward modelling stage. However, this method still heavily relies on the first approximation.In this paper we try to gain a deeper theoretical understanding of these practical algorithms. In particular we derive a new general objective called ΨΨPO for learning from human preferences that is expressed in terms of pairwise preferences and therefore bypasses both approximations. This new general objective allows us to perform an in-depth analysis of the behavior of RLHF and DPO (as special cases of ΨΨPO) and to identify their potential pitfalls. We then consider another special case for ΨΨPO by setting ΨΨ simply to Identity, for which we can derive an efficient optimisation procedure, prove performance guarantees and demonstrate itsempirical superiority to DPO on some illustrative examples.
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