onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction
Paper recorded by Signals 4 on 2026-09-21 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22
Category: cs.LG · 机器学习 · first seen 2026-09-22
Abstract
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 ever
Read on arXiv →
Cite this page: onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction: the #2 most recent of 250 cs.LG papers we have recorded (as of 2026-09-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/onpanda-efficient-annotation-of-on-policy-alignment-data-for-llms-and-agents-via.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable:
papers.json
Get 4 AI signals a day by email — free.
Get 4 AI signals a day by email — free