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

Junwon Ko; Dong-Jae Lee; Minchan Kwon; Sunghyun Baek; Junmo Kim

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arXiv · 2609.10052

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

Junwon Ko·Dong-Jae Lee·Minchan Kwon·Sunghyun Baek·Junmo Kim

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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.

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Junwon Ko, Dong-Jae Lee, Minchan Kwon, Sunghyun Baek, Junmo Kim. 2026-09-09. Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training. https://arxiv.org/abs/2609.10052

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