arXiv:2607.01225v1 Announce Type: new Abstract: Prior work on imitation learning from suboptimal demonstrations typically relies on compressed supervision signals such as confidence estimates, discriminator scores, or importance weights. These scalar signals are inherently limited, as they cannot explicitly express intermediate reasoning about task progress, failure modes, or corrective actions. We propose a language-critique framework for imitation learning from suboptimal demonstrations that instead leverages natural language as a structured supervision signal, avoiding the collapse of expre
This research builds on recent progress in large language models and reinforcement learning, leveraging their expressive capabilities to address a known limitation in imitation learning from suboptimal demonstrations.
Improving the efficiency of learning from imperfect human data significantly accelerates AI development, particularly for complex tasks where perfect demonstrations are scarce, leading to more robust and capable AI systems.
The ability to use natural language critiques as a structured supervision signal bypasses the inherent limitations of scalar signals, enabling AI to better understand and correct its own errors based on qualitative feedback.
- · AI developers
- · Robotics companies
- · Autonomous systems
- · Companies with complex human-in-the-loop processes
- · Traditional imitation learning methods
- · Systems highly reliant on perfectly curated datasets
- · Manual data labelling services for scalar feedback
AI models will learn more effectively from imperfect human operation, accelerating the development cycle for agentic systems.
This improved learning efficiency could lead to faster deployment of AI agents in real-world, dynamic environments across various sectors.
More sophisticated and reliably trained AI agents might begin to automate a wider array of complex, cognitive tasks previously thought to require extensive human oversight or perfect training data.
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Read at arXiv cs.LG