---
title: "Scalable Behaviour Cloning on Browser via Skill Distillation"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2606.32014v1"
permalink: "https://feed.astralor.com/items/itm_acd9bcf147f53f4f567961f4"
published: "2026-06-30T17:46:23.000Z"
score: 53
channel: "ai"
tags: ["Scalability","skill graph","Skill Distillation","browser agent","Behaviour Cloning"]
---

# Scalable Behaviour Cloning on Browser via Skill Distillation

## At a glance

This paper proposes converting user browser interaction trajectories into compact natural-language skills via skill distillation, enabling browser agents to read, retrieve, reuse, and compose these skills directly, overcoming the bottleneck of decision-making under incomplete information. The authors further organize distilled skills into a skill graph, allowing growth through consolidation rather than unbounded accumulation, suggesting that the scalability of browser agents may come more from the collective skills of Internet users than from manually designed tasks.

## Key points

- Proposes skill distillation to extract natural-language skills from human browser interaction traces.
- Organizes skills into a skill graph for consolidation-based growth rather than unbounded accumulation.
- Demonstrates that agents can leverage collective user skills for decision-making.

## Full text

Internet users collectively perform an enormous range of skilled work through web browsers, from software development and document editing to search, forms, and enterprise workflows, making human browsing a highly scalable but under-exploited source of reusable browser skills. We argue that the bottleneck for browser agents is decision-making under incomplete information rather than low-level operation, and that the priors agents lack are already implicit in human interaction traces. We therefore study scalable behavior cloning for browser agents via skill distillation, converting user interaction trajectories into compact natural-language skills that agents can read, retrieve, reuse, and compose directly. We further organize the distilled skills into a skill graph so that growth proceeds through consolidation rather than unbounded accumulation. This suggests that the scalability of browser agents may come less from manually designed tasks and more from the collective skills already expressed by internet users. Our project is available at: https://lab.einsia.ai/browserbc/.

---

Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_acd9bcf147f53f4f567961f4