Topic: Reinforcement Learning

We highlight the introduction of SmartSnap, a novel reinforcement learning training method that transforms GUI agents from passive executors into proactive self-verifiers. Instead of relying on complex external supervision or lengthy trajectory reviews, this framework enables agents to curate an evidence snapshot set following the 3C principles of completeness, conciseness, and creativity. Our analysis shows that this approach significantly reduces verification overhead, requiring an average of only 1.5 screenshots per task to confirm completion. Experimental results on AndroidLab demonstrate performance gains of up to 26.08%, remarkably allowing mid-sized models like Qwen3-32B to match the capabilities of massive models such as DeepSeek-V3 and Qwen3-235B. This shift towards proactive evidence seeking simplifies RL training for dynamic environments like mobile operating systems where state feedback is often transient or difficult to capture, marking a transition from brute-force execution to cognitive synergy.

We introduce LENS (Learning to Segment Anything with Unified Reinforced Reasoning), a novel framework accepted as an Oral paper at AAAI 2026. Traditional image segmentation models relying on Supervised Fine-Tuning often hit a 'capability ceiling' due to static pattern matching and information bottlenecks between reasoning and execution. To overcome this, we implement an end-to-end reinforcement learning mechanism that co-optimizes high-level Chain-of-Thought reasoning with pixel-level segmentation. By utilizing a Multi-modal Large Language Model like Qwen2.5-VL-3B-Instruct and a dedicated Context Module, LENS bridges the gap between 'thinking' and 'acting,' enabling self-correction even from imperfect initial prompts. This architecture significantly enhances generalization and robustness in complex, open-world scenarios. We believe this advancement offers a strategic path for developing more sophisticated embodied AI and human-robot interaction systems.