arXiv:2606.05152v1 Announce Type: new Abstract: Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sample many responses and reward each with a single bit indicating whether the final answer is correct. Yet many settings provide rich feedback, including execution traces, tool outputs, expert corrections, and model self-evaluations. We study how to use such feedback through a distributional variant of the classic imitation learning algorithm DAgger, where the learner has local access to an expert dis
The paper directly addresses a known limitation in current LLM training paradigms, building on recent advances in reasoning models and the increasing availability of richer feedback types.
This work represents a key step in advancing AI model training beyond simplistic single-bit rewards, enabling more sophisticated and efficient learning from complex expert demonstrations and system outputs.
The ability to leverage rich feedback like execution traces and expert corrections will lead to more robust and less error-prone AI systems, particularly in agentic applications requiring multi-step reasoning.
- · AI developers
- · AI-driven automation companies
- · Robotics
- · SaaS providers leveraging AI
- · Companies reliant on simple RLHF
- · Companies with inefficient AI training pipelines
More capable and reliable AI models, especially for complex tasks.
Accelerated development of AI agents capable of autonomous decision-making and execution in real-world environments.
Significant reduction in human oversight required for many automated processes, leading to faster digital transformation across industries.
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Read at arXiv cs.LG