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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unreviewed preprint describing Direct Diversity Optimization (DDO), a novel offline post-training method for LLM agents that aims to preserve multiple successful strategies during sequential decision-making. The method was evaluated on three simulated environments and reported higher task success and strategy coverage than comparison methods, but lacks peer review, real-world validation, and quantitative reporting of effect sizes or statistical significance.
Algorithm development with empirical comparison on simulated environments. LLM agents in sequential decision-making tasks using trajectory-level outcome labels during post-training. Intervention: Direct Diversity Optimization (DDO): offline post-training method combining Divergence-Tree Collection (DTC) and Reference-Relative Target-Odds Objective (RTO). Compared with: Successful-only imitation, decoding-time diversification, and other unspecified post-training methods.
DDO achieves strongest task success and successful strategy coverage among compared post-training methods across BabyAI, BabaIsAI, and WebShop DDO achieves highest recovery rate after local action replacement DDO shows higher task success and coverage than successful-only imitation and decoding-time diversification controls
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Early-stage algorithmic work on a new post-training method for LLM agents, tested on simulation environments without peer review; demonstrates feasibility but lacks clinical or real-world validation and has not undergone peer review.
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LLM agents for sequential decision tasks are often post-trained with trajectory-level outcome labels, but such labels provide little supervision for preserving multiple successful branches from the same decision state. We study this problem as successful strategy coverage: how broadly a model realizes distinct successful strategies under a fixed rollout budget. We present Direct Diversity Optimization (DDO), an offline post-training method that combines Divergence-Tree Collection (DTC) with the Reference-Relative Target-Odds Objective (RTO). DTC constructs state-aligned branch sets rooted at shared decision states, and RTO trains the model to match reference-relative targets over successful alternatives. DDO achieves the strongest task success and successful strategy coverage among the compared post-training methods across BabyAI, BabaIsAI, and WebShop. It also achieves the highest recovery rate after local action replacement and higher task success and coverage than successful-only imitation and decoding-time diversification controls.
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