arXiv:2607.01897v1 Announce Type: new Abstract: We introduce Rank-Then-Act (RTA), a framework for learning control policies from expert video demonstrations without environment rewards. RTA trains a Vision-Language Model (VLM) offline as a progress-based ordinal scorer, using a Group Relative Policy Optimization (GRPO) objective over shuffled frame sequences, which forces the model to recover temporal ordering from visual semantics rather than trivial time cues. Importantly, instead of using the scorer directly as a scalar reward model, we propose a correlation-based reward function for reinfo
The continuous advancements in Vision-Language Models (VLMs) and the increasing demand for data-efficient, reward-free learning in complex environments make this development timely.
This research provides a novel method for training control policies without explicit reward functions, significantly reducing the cost and complexity of developing AI agents in real-world scenarios.
The ability to learn control policies from unstructured video data could accelerate the development of autonomous systems by bypassing the need for tedious reward engineering or human labelling.
- · AI agents developers
- · Robotics industry
- · Automation sector
- · Simulation platforms
- · Traditional reward engineering services
- · Dataset labelling companies (for reward signals)
More sophisticated and versatile AI agents can be developed with less expert input and simplified pre-training.
The proliferation of contextually aware autonomous agents could drive efficiency gains across various industries, from logistics to manufacturing.
Reduced barriers to entry for AI agent development may lead to rapid innovation in new applications of autonomous systems, potentially accelerating demand for compute infrastructure.
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Read at arXiv cs.LG