arXiv:2605.30861v1 Announce Type: new Abstract: Post-training for reasoning models typically combines supervised fine-tuning with reinforcement learning from verifiable rewards, most commonly with GRPO. However, this algorithm suffers from sparse rewards, limited exploration, and mode collapse. Building upon recent works on self-distillation, we propose Feedback Distillation, a training method where the model is trained to match, at the token level, its own distribution conditioned on privileged feedback produced by a language model. Feedback Distillation offers token-level supervision and can
This research addresses fundamental limitations in current LLM training for reasoning, offering a novel token-level supervision method to overcome sparse rewards and mode collapse, crucial for advancing AI capabilities.
Improved theorem proving and general reasoning capabilities in LLMs could significantly accelerate scientific discovery, software development, and the robustness of AI systems, impacting numerous high-value sectors.
The proposed 'Feedback Distillation' method changes how LLMs learn to reason by providing richer, token-level feedback, potentially leading to more efficient and capable reasoning models compared to existing reinforcement learning approaches.
- · AI research institutions
- · Software development
- · Scientific research
- · AI agent developers
- · Companies reliant on less sophisticated AI reasoning
- · Traditional theorem proving methods
More powerful and reliable AI models become available for complex, symbolic tasks.
Automation of highly complex intellectual tasks, such as formal verification and advanced programming, accelerates significantly.
The development of truly autonomous AI agents capable of self-correcting and high-level abstract reasoning becomes more feasible, impacting various white-collar workflows.
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Read at arXiv cs.AI