"Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration"

Teaching LLMs human preferences just got faster by mixing past knowledge with active learning.

HPO combines offline preference datasets with online exploration for more efficient RLHF training, achieving faster convergence by leveraging existing data while exploring new responses.

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https://arxiv.org/abs/2412.10616

🤔 Original Problem:

RLHF faces a dilemma - offline methods need strict data requirements while online exploration is expensive and time-consuming. Neither approach alone is optimal for aligning LLMs with human preferences.

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🔧 Solution in this Paper:

→ Introduces Hybrid Preference Optimization (HPO) that merges offline preference data with targeted online exploration

→ Uses Sequential Exploration Coefficient to measure and optimize exploration efficiency

→ Implements optimistic exploration through regularization terms that encourage policy diversity

→ Updates policy using both offline and online feedback in each training iteration

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💡 Key Insights:

→ HPO achieves O(sqrt(d_hyb*d/T)) convergence rate for linear MDPs

→ No strict concentrability requirements on offline data unlike pure offline methods

→ Reduces sample complexity compared to both pure online and offline approaches

→ Works effectively even with suboptimal offline datasets

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📊 Results:

→ Shows significantly lower cumulative regret vs online baselines

→ Achieves better suboptimality gaps than both online and offline methods

→ Successfully utilizes offline data even when pure offline methods fail