Argos: A New Reward Agent for Multimodal Reinforcement Learning
A recent study presents Argos (Agentic Reward for Grounded & Objective Scoring), a reward agent aimed at enhancing the training of multimodal reasoning models for agentic tasks. This research, accessible on arXiv (2512.03438), tackles the issue of sparse, outcome-oriented rewards in multimodal reinforcement learning (MMRL). Argos evaluates the accuracy of final responses and the spatiotemporal localization of references by selecting from a variety of scoring functions derived from teacher models and rules. This methodology seeks to offer more detailed guidance throughout the training process, which could improve the performance of agentic reasoning models.
Key facts
- The paper introduces Argos, a reward agent for multimodal reinforcement learning.
- Argos is designed for agentic tasks.
- It selects from teacher-model derived and rule-based scoring functions.
- It evaluates final response accuracy and spatiotemporal localization.
- The paper is available on arXiv with ID 2512.03438.
- The approach aims to improve learning by providing more informative rewards.
- The paper is a replacement (v3) of the original submission.
Entities
Institutions
- arXiv