Input-Output Sampling for Efficient Multi-Objective Molecular Optimization with LLMs

Multi-objective molecular optimization with LLMs is bottlenecked by limited oracle budgets. Existing methods often struggle to maintain stable trade-offs among competing properties, and to select samples that provide informative learning signals, causing expensive evaluations to be spent on redundant molecules. To this end, we propose MolDAS, a multi-objective reinforcement learning framework with Dual-sided Adaptive Sampling that selects original molecules at the input side and optimized candidates at the output side. At the input level, MolDAS balances structural diversity with proximity to the policy's current capability boundary, selecting molecules that are underexplored but still learnable for oracle evaluation. At the output level, it sends the lowest-probability answer segments in each rollout group to the oracle. Moreover, it identifies high-value completions within rollout groups via reward dispersion and applies an auxiliary supervised fine-tuning loss for direct imitation. To further stabilize training, a lightweight dynamic weighting scheme calibrates property improvement, structural similarity, and reasoning quality as optimization progresses. MolDAS is trained in a two-stage simulator-to-oracle paradigm, first with an inexpensive simulator and then adapted under a capped budget of high-fidelity oracle calls that mirrors real experimental constraints. Experiments show that MolDAS raises the learning ceiling under abundant low-fidelity feedback and learns faster under limited high-fidelity feedback, achieving a 3.7% relative improvement over vanilla GRPO in Stage 1 and over 16.1% in Stage 2 on the optimization score. Sampling audits show reduced structural redundancy and higher molecular diversity, and lower performance fluctuation than fixed-weight training. These results suggest that our method offers a practical route to efficient and stable multi-objective molecular optimization with LLMs.