ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning
Agentic systems have rapidly advanced in their ability to interact with real-world environments, leverage external tools, and provide services for users. However, unlike natural-world tasks that assume well-defined instructions, human-centered scenarios are characterized by ambiguous requests that lead to large, open-ended solution spaces. Decoding users' personalized preferences is therefore essential for narrowing the candidate solution space. This introduces a new challenge, personalized agentic reasoning, which requires agents to jointly interact with both users and environments to deliver personalized services. In this paper, we present ODYSSE, a Reinforced Fine-Tuning (RFT) framewor...
| Authors | Jiaqi Zhang, Tong Chen, Junliang Yu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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