Paper page - PANDO: Efficient Multimodal AI Agents via Online Skill Distillation

Source: https://huggingface.co/papers/2605.24785

Abstract

PANDO is a web agent framework that improves efficiency through experience accumulation by reducing redundant actions, optimizing skill discovery, and enhancing prompt caching without sacrificing performance.

Recent advances inmultimodal web agentsoften rely on increased inference-time computation, includingrollout search,verifier passes,offline skill discovery, andspecialist model stacks. This raises a central question: can a web agent become more efficient as it accumulates experience, rather than more expensive? We first analyze trajectories fromVisualWebArenaand identify three recurring sources of inefficiency: repeat-action loops, hidden discovery costs, and low prompt-cache reuse. We then introduce PANDO, a single-rollout onlineskill-distillation frameworkthat maintains a structuredSkill Libraryand combinesprogress reflection,confidence-based skill demotion,hierarchical routing,visual compression, andcache-aware prompting. On the full set of 910VisualWebArenatasks, PANDO achieves a 58.3% success rate, outperforming SGV (54.0%) and our WALT reproduction (45.2%), while using 58% fewer tokens than SGV and 61% fewer tokens than WALT, without any pre-evaluation discovery budget. A 300-task ablation further shows that rules and routines provide most of the success gains, while routing, compression, andcache-aware promptingconvert the largerskill libraryinto lower marginal token cost. Finally, we introduce three trajectory-level efficiency metrics --Action Repetition Rate,Step Overhead Ratio, andPrompt Cache Utilization-- to make efficiency visible beyond terminal success.

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