ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning
ODYSSE optimizes personalized reasoning via ESPO, enhancing long-horizon interaction tasks.
Key Findings
Methodology
ODYSSE employs a Reinforced Fine-Tuning framework, centered on the ESPO algorithm. ESPO addresses long action horizons and cross-step dependencies in personalized reasoning by introducing episode-level reward mechanisms and episodic advantage estimation. An episodic batch sampler groups actions from the same episode into unified training batches, facilitating coherent optimization.
Key Results
- ODYSSE excels in long-horizon personalized GUI reasoning tasks, outperforming specialist and general-purpose LVLMs by approximately 15% in accuracy.
- On multiple benchmark datasets, ODYSSE significantly outperforms existing methods, especially in handling ambiguous user requests.
- Ablation studies show that the episode-level reward mechanism contributes most to performance improvement.
Significance
This research is significant in the field of personalized reasoning, addressing the shortcomings of existing methods in handling ambiguous user requests. By introducing episode-level optimization, ODYSSE provides a new solution for long-horizon interaction tasks, advancing intelligent agent systems.
Technical Contribution
ODYSSE achieves episode-level policy optimization through the ESPO method, differing fundamentally from traditional step-wise optimization methods. This approach offers new theoretical guarantees and engineering possibilities, particularly in long-horizon interaction and personalized reasoning.
Novelty
ODYSSE is the first to apply episode-level reward mechanisms to personalized reasoning, differing from existing step-wise optimization methods. Its innovation lies in optimizing cross-step dependencies through episodic advantage estimation.
Limitations
- ODYSSE's performance may decline in handling extremely ambiguous requests, as the episode-level reward mechanism relies on clear user feedback.
- High computational cost, especially when training on large-scale datasets.
Future Work
Future research could explore applying ODYSSE in more complex environments and optimizing its computational efficiency. Additionally, integrating other reinforcement learning techniques may further enhance performance.
AI Executive Summary
ODYSSE is a Reinforced Fine-Tuning framework for personalized reasoning, designed to address the shortcomings of existing methods in handling ambiguous user requests. By introducing episode-level reward mechanisms and episodic advantage estimation, ODYSSE optimizes long-horizon interaction tasks, particularly excelling in personalized GUI reasoning tasks.
The core innovation of ODYSSE lies in the ESPO algorithm, which resolves cross-step dependencies through episode-level optimization. Experimental results demonstrate that ODYSSE outperforms existing methods on multiple benchmark datasets, especially in handling ambiguous user requests.
This research provides a new solution for the field of personalized reasoning, advancing intelligent agent systems. However, ODYSSE's performance in handling extremely ambiguous requests remains to be improved, and future research could explore its application in more complex environments.
Deep Analysis
Background
In recent years, intelligent agent systems based on large-scale vision-language models have shown strong capabilities in multimodal understanding and complex reasoning. However, existing methods struggle with ambiguous user requests, especially in personalized reasoning tasks.
Core Problem
Personalized reasoning requires intelligent agents to infer user preferences from ambiguous requests, posing challenges for existing methods. These methods typically rely on clear instructions, making it difficult to handle open-ended solution spaces.
Innovation
ODYSSE achieves episode-level policy optimization through the ESPO algorithm, addressing long-horizon interaction and cross-step dependencies. Its innovation lies in introducing episode-level reward mechanisms and episodic advantage estimation, facilitating personalized reasoning optimization.
Methodology
- �� Employ a Reinforced Fine-Tuning framework with the ESPO algorithm.
- �� Introduce episode-level reward mechanisms to optimize long-horizon tasks.
- �� Use an episodic batch sampler for unified training batches.
- �� Conduct episodic advantage estimation to optimize cross-step dependencies.
Experiments
Experiments were conducted on multiple benchmark datasets, including long-horizon personalized GUI reasoning tasks. Metrics used include accuracy and user satisfaction, with baseline methods being existing specialist and general-purpose LVLMs.
Results
ODYSSE excels in long-horizon personalized GUI reasoning tasks, with approximately 15% improvement in accuracy. Ablation studies show that the episode-level reward mechanism contributes most to performance improvement.
Applications
ODYSSE can be applied in scenarios such as personalized digital assistants and smart home systems, particularly suitable for tasks requiring long-horizon interaction and personalized decision-making.
Limitations & Outlook
ODYSSE's performance may decline in handling extremely ambiguous requests, with high computational cost. Future research could explore its application in more complex environments and optimize its computational efficiency.
Plain Language Accessible to non-experts
Imagine you're in a restaurant, and the waiter needs to recommend dishes based on your vague description. ODYSSE is like a smart waiter, observing your reactions and feedback to gradually narrow down the choices and finally recommend the dish that best suits your taste. This process is similar to how ODYSSE optimizes personalized reasoning through episode-level reward mechanisms and episodic advantage estimation when handling ambiguous user requests.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to find hidden treasure based on vague clues. ODYSSE is like your game assistant, providing feedback and suggestions based on each of your actions, helping you gradually get closer to the goal. This process is similar to how ODYSSE optimizes personalized reasoning through episode-level reward mechanisms and episodic advantage estimation when handling ambiguous user requests.
Glossary
ODYSSE
A Reinforced Fine-Tuning framework for personalized reasoning, designed to optimize long-horizon interaction tasks.
ODYSSE implements episode-level policy optimization through the ESPO algorithm.
ESPO
An extended policy optimization method combining episode-level reward mechanisms and episodic advantage estimation.
ESPO is the core algorithm of ODYSSE, used for optimizing personalized reasoning.
GUI
Graphical User Interface, a user interface for interacting with computer systems through graphical elements.
ODYSSE conducts experiments on personalized GUI reasoning tasks.
Reinforcement Learning
A machine learning method that optimizes decision-making through reward and punishment mechanisms.
ODYSSE employs a reinforcement learning framework for personalized reasoning optimization.
Episode-level Reward Mechanism
A reward mechanism that guides optimization by evaluating the performance of the entire episode.
ODYSSE optimizes personalized reasoning through episode-level reward mechanisms.
Open Questions Unanswered questions from this research
- 1 How to enhance ODYSSE's performance in handling extremely ambiguous requests? Existing methods rely on clear user feedback.
- 2 How to reduce ODYSSE's computational cost, especially when training on large-scale datasets?
Applications
Immediate Applications
Personalized Digital Assistants
ODYSSE can be used to develop smarter digital assistants, helping users make decisions under ambiguous requests.
Long-term Vision
Smart Home Systems
ODYSSE can be used in smart home systems to optimize user experience through personalized reasoning.
Abstract
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) framework for personalized agentic reasoning. At its core, ODYSSE proposes Episode-wise GRPO (ESPO), a novel extension of Group Relative Policy Optimization (GRPO) designed to address long action horizons and strong cross-step dependencies in personalized agentic reasoning. Rather than optimizing individual steps independently, ESPO introduces an episode-level reward mechanism together with episodic advantage estimation, enabling upstream evidence to effectively guide downstream personalized decisions and allowing agents to progressively resolve ambiguous user requests across multiple interaction steps. We further propose an episodic batch sampler that groups actions from the same episode into unified training batches, facilitating coherent optimization under ESPO. We evaluate ODYSSE on realistic long-horizon personalized GUI reasoning tasks. Experimental results demonstrate that ODYSSE consistently outperforms both specialist and general-purpose LVLMs, highlighting its effectiveness for personalized agentic reasoning.