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Machine Learning

Reinforcement Learning from Rich Feedback with Distributional DAgger

Rishabh Agrawal, Jacob Fein-Ashley, Paria Rashidinejad

Featured June 24, 2026

Original Paper

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Simply

This paper introduces DistIL, a new learning method that helps AI models learn better from detailed feedback, like error messages or step-by-step solutions, by ensuring each learning step actually improves the model and correctly links early choices to later outcomes.

In depth
The paper introduces DistIL, a novel reinforcement learning approach that leverages rich feedback (e.g., execution traces, expert corrections) to train reasoning models. Unlike prior methods that use sparse rewards or -divergences, DistIL optimizes a forward cross-entropy objective, guaranteeing monotonic policy improvement and performing future-aware credit assignment to effectively learn from detailed guidance.

Key Takeaways

  • 1
    Existing on-policy self-distillation methods using -divergences (e.g., reverse KL) do not guarantee monotonic policy improvement, potentially decreasing reward even when the teacher is superior.
  • 2
    Prior methods' reliance on local, token-wise gradient approximations fails to provide future-aware credit assignment, leading to suboptimal policies by ignoring how early decisions impact future states.
  • 3
    DistIL addresses these limitations by employing a forward cross-entropy objective and full sequence-level gradients, ensuring reward-aligned updates, sublinear regret, and superior empirical performance across various reasoning tasks.

Conceptual Flow

HIGH LEVEL
1
Methodology: Learning from Detailed Guidance

The new method learns by comparing its actions to an expert's detailed guidance at each step, making sure it improves steadily and understands how early choices affect later results.

Model's Current Actions
Detailed Expert Guidance
Compare and Learn
Improved Model Actions
2
Results: Superior Performance in Complex Tasks

The new approach consistently performs better than older methods in complex tasks like science, coding, and math, showing more stable learning and higher success rates.

Old Learning Methods
New DistIL Method
Solve Complex Problems
Lower Success Rate
Higher Success Rate