Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training

Abstract: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.

Accepted to EMNLP 2026 Main Conference. 19 pages, 11 figures

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

arXiv:2609.10052 [cs.CL]

(or arXiv:2609.10052v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.10052 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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