ForgeLoc-R1: Set-Level Reinforcement Fine-Tuning of Video MLLMs for Temporal Forgery Localization

Temporal forgery localization predicts every manipulated interval in a partially forged video. Existing methods rely on detectors built for particular manipulation traces, whereas a general-purpose video multimodal large language model (MLLM) could localize any forgery family. Reinforcement fine-tuning with GRPO and an overlap reward is the standard recipe for temporal grounding, but transplanted to forgery localization it loses its learning signal. We show that both losses are inherent in the group-relative update: a group whose sampled intervals miss or barely overlap a short segment yields rewards that differ only by sampling noise, and the update is biased toward the segment count most frequent in the training data. ForgeLoc-R1restores the signal at both points while leaving the GRPO update unchanged, and scores each rollout with a set reward that the two mechanisms act on. 1)At the group level, anchor-guided advantage pruning inserts the ground-truth answer into every group as an anchor, which we show gives a group without signal a likelihood step toward the ground truth. Reward shaping then bounds the anchor's margin, and only the anchor and the rollouts with the largest advantage magnitudes are trained on. 2)At the reward level, a set-level reward counts the distinct segments a rollout has found, so a missed segment and an unmatched prediction both lower it, and ranks the misses by their distance to the ground truth. 3)At the objective level, a count-balanced objective weights multi-segment videos so that the policy is not biased toward the most frequent segment count. On ActivityForensics and DDL, ForgeLoc-R1 outperforms the strongest reinforcement fine-tuning baseline by 4.34 and 3.11 mIoU, with the largest gains on multi-segment videos and on a generator held out from training.