onPanda：token 级纠正标注对齐数据，中位标注时间省 52%
onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction
标注对齐数据不用整段重写，定位到第一个错 token 改掉让模型接着生成，实验里中位时间省 52%，还附了 Panda-CVL 数据集。
论文提出交互式标注工具 onPanda，用 token 级纠正来标注 LLM 对齐数据和智能体轨迹：标注员定位首个不当 token，可从候选 token 中选替换项或自由改写。系统随即截断其后内容并从修正前缀继续生成，循环执行定位-纠正-续写直到产出满意回复。小规模对照实验显示，onPanda 相比人工后期编辑将中位标注时间减少 52%。由于最终回复的绝大多数 token 由模型自身生成，产出数据基本保留模型采样分布，适合构建 on-policy SFT 与偏好数据；纠正记录还带精确位置、天然构成正负样本对，可作细粒度监督。团队同时发布用 onPanda 标注的 Panda-CVL 数据集和一个 token 级纠正基准。
onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction
We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction as its core interaction: while reading a model response, the annotator locates the first inappropriate token and either picks a substitute from the model's candidate tokens or types the correct text via free-form editing. The system then truncates everything after that position and continues generation from the corrected prefix, repeating this locate-correct-continue loop until a satisfactory response is obtained. This mechanism lets annotators precisely steer model outputs at low cost: a small controlled study suggests that onPanda reduces median annotation time by 52% over manual post-editing. Since the vast majority of tokens in the final response are generated by the model itself, the resulting data largely preserves the model's sampling distribution and is well suited for constructing on-policy SFT and preference data. Furthermore, the token-level corrections recorded during annotation provide fine-grained supervision with precise positions and naturally paired positive--negative samples. onPanda also connects to external tools and harnesses, enabling interactive trajectory annotation in realistic environments. In addition, we release Panda-CVL, a dataset annotated with onPanda, together with a benchmark for token-level correction.