WebKD：黑盒教师按学生风格改写失败轨迹来蒸馏网页智能体
Teaching Agents in their Own Style: Blackbox Teacher Distillation for Web Agents
闭源模型当老师也能蒸馏：只改错处、照学生的口吻写，TimeWarp 上比行为克隆高 5-10 个点。
Token 级蒸馏需要读取教师模型的 logits，闭源教师做不到，常规做法是离线模仿教师示范。论文提出 WebKD：给定一段失败轨迹，教师找到第一个出错点，从那里开始改写学生的推理、harness 状态和动作。它坚持最小编辑——教师用学生自己的风格写修正，推理按其对动作的贡献加权，再用角色非对称 KL 锚定让动作自由移动、推理贴近学生原有分布。在 TimeWarp 基准上，WebKD 学生比行为克隆、结果奖励 RL 和教师改写基线高约 5.5-10.7 个百分点。该能力可迁移到未见过的版本和真实网页的 OnlineMind2Web 基准。
Teaching Agents in their Own Style: Blackbox Teacher Distillation for Web Agents
Frontier web agents using closed models have high inference and computational costs. Smaller open models can be an alternative, but to be performant, they require training supervision from a human or teacher model. Token-level teacher distillation is not an option for closed teachers as it needs access to the model's logits. The standard approach is to imitate teacher demonstrations offline, but it trains the student on states it has never visited, and in a style it does not write in. We introduce WebKD, where the teacher is the editor of the student's failures. Given a failed episode, the teacher finds the first point of mistake, and rewrites the student's reasoning traces, harness states, and actions from that point onward. The editing reveals that the teacher's corrections can be more surprising to the student than the student's own text. WebKD instead aims to edit minimally. In the data space, the teacher writes each correction in the student's style. In the objective space, the reasoning is weighted by its contribution to the action. A role-asymmetric KL anchor allows the corrected action to move freely while keeping the reasoning near the student's prior. On the TimeWarp benchmark, WebKD students gain about 5.5-10.7 pp over behavior cloning, outcome-reward RL, and teacher editing baselines. The performance transfers to unseen versions and to the live web in the OnlineMind2Web benchmark. WebKD opens a new way of distilling black-box models to improve the capabilities of open-weight web agents.
