MARCO: Multi-Round Agentic Reinforcement for Conditional Molecular Optimization

Abstract:Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relationship to the source molecule. Most instruction-following models instead emit one edited molecule, forcing validity, property improvement, and similarity control into a single response. We introduce MARCO, an evaluator-grounded reinforcement-learning framework that trains molecular editors on bounded proposal--feedback--revision trajectories. MARCO aggregates shaped turn rewards into an undiscounted trajectory return for group-relative policy optimization. We evaluate two consequences of this training: Same-1 tests the trained policy under a one-response budget, while Same-5 tests whether the same policy can use verifier feedback when up to five responses are available. Across the three-objective MuMOInstruct benchmark, three Qwen backbones, and seen/unseen instruction splits, SFT-initialized MARCO obtains the highest product of property success rate and similarity in every reported primary setting. Same-5 further improves the observed score under the tested budget, while four-objective and public-checkpoint experiments test transfer across constraint sets and initialization regimes.

Machine Learning (cs.LG); Computation and Language (cs.CL)

arXiv:2609.36683 [cs.LG]

(or arXiv:2609.36683v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.36683 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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