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[2602.19372] Seeing Farther and Smarter: Value-Guided Multi-Path Reflection for VLM Policy Optimization
Summary
The paper presents a novel framework for optimizing Vision-Language Models (VLMs) in robotic manipulation tasks, enhancing decision-making through multi-path reflection and improved state evaluation.
Why It Matters
This research addresses significant limitations in current VLM approaches for robotic tasks, such as inefficiency and high inference latency. By proposing a more effective framework, it contributes to advancements in robotics and AI, potentially leading to more reliable and faster robotic systems in real-world applications.
Key Takeaways
- Introduces a framework that decouples state evaluation from action generation for better decision-making.
- Implements beam search to explore multiple future paths, enhancing robustness in action generation.
- Demonstrates a 24.6% improvement in success rates and a 56.5% reduction in inference time over existing methods.
Computer Science > Robotics arXiv:2602.19372 (cs) [Submitted on 22 Feb 2026] Title:Seeing Farther and Smarter: Value-Guided Multi-Path Reflection for VLM Policy Optimization Authors:Yanting Yang, Shenyuan Gao, Qingwen Bu, Li Chen, Dimitris N.Metaxas View a PDF of the paper titled Seeing Farther and Smarter: Value-Guided Multi-Path Reflection for VLM Policy Optimization, by Yanting Yang and 4 other authors View PDF HTML (experimental) Abstract:Solving complex, long-horizon robotic manipulation tasks requires a deep understanding of physical interactions, reasoning about their long-term consequences, and precise high-level planning. Vision-Language Models (VLMs) offer a general perceive-reason-act framework for this goal. However, previous approaches using reflective planning to guide VLMs in correcting actions encounter significant limitations. These methods rely on inefficient and often inaccurate implicit learning of state-values from noisy foresight predictions, evaluate only a single greedy future, and suffer from substantial inference latency. To address these limitations, we propose a novel test-time computation framework that decouples state evaluation from action generation. This provides a more direct and fine-grained supervisory signal for robust decision-making. Our method explicitly models the advantage of an action plan, quantified by its reduction in distance to the goal, and uses a scalable critic to estimate. To address the stochastic nature of single-trajec...
