onPanda Paper Introduces Token-Level Correction for On-Policy Alignment Data
Token-level correction can cut annotation cost because reviewers mark specific tokens instead of full responses, which changes how teams budget for on-policy data pipelines. Practitioners building RLHF or agent fine-tuning workflows can apply it to reduce labelling overhead and compare it against response-level annotation baselines.
