POLO
POLO: Preference-Guided Multi-Turn Reinforcement Learning for Sample-Efficient Lead Optimization (KDD 2026 Oral)
POLO (Preference-guided multi-turn Optimization for Lead Optimization) enables LLMs to learn from complete optimization trajectories rather than isolated steps. This repository contains the implementation of POLO, which introduces Preference-Guided Policy Optimization (PGPO), a novel reinforcement learning algorithm that extracts learning signals at two complementary levels: trajectory-level optimization reinforces successful strategies, while turn-level preference learning provides dense comparative feedback by ranking intermediate molecules within each trajectory.
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curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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