DistIL: A Distributed DAgger Method Using Rich Feedback to Break Through Reinforcement Learning Bottlenecks

DistIL: A Distributed DAgger Method Using Rich Feedback to Break Through Reinforcement Learning Bottlenecks

Researchers propose the DistIL method, which leverages a distributed DAgger algorithm and a forward cross-entropy objective function to effectively utilize rich feedback signals such as execution trajectories and tool outputs, outperforming traditional RLVR baselines in scientific reasoning, programming, and mathematical problem-solving domains.

Research Background: Limitations of RLVR

In recent years, reasoning models have developed rapidly, but the underlying training method—Reinforcement Learning with Verifiable Rewards (RLVR)—has limitations due to its binary reward mechanism, ignoring rich feedback signals like execution trajectories, tool outputs, expert corrections, and model self-assessments. How to effectively use these signals for model training remains an open question.

DistIL Method: Innovation of DAgger from a Distributed Perspective

The core innovations of DistIL are the distributed DAgger framework (accessing expert distribution instead of single optimal actions) and the forward cross-entropy objective function (sequence-level gradient propagation for fine-grained credit assignment). Distributed DAgger provides richer supervision, better exploration guidance, and compatibility with black-box experts; forward cross-entropy can trace errors in intermediate steps.

Theoretical Guarantees: Monotonic Policy Improvement and Regret Bounds

Traditional self-distillation objectives cannot guarantee monotonic policy improvement, while DistIL’s forward cross-entropy objective has theoretical advantages: 1. Monotonic policy improvement; 2. Regret bound guarantees; 3. Optimization of success probability lower bounds, providing a foundation for reliability.

Experimental Validation: Cross-Domain Performance Improvement

DistIL’s effectiveness is validated across multiple domains:

• Scientific reasoning: Understands key steps in reasoning chains, outperforming RLVR;
• Programming tasks: Uses feedback like compiler errors to accelerate learning;
• Mathematical problems: Identifies key turning points in problem-solving and avoids wrong paths.

Practical Significance and Application Prospects

DistIL’s practical value includes: reducing data annotation costs (using low-cost rich feedback), improving training stability (monotonic improvement guarantee), promoting human-machine collaboration (black-box expert compatibility), and can be extended to robot control, game AI, and dialogue systems.

Limitations and Future Directions

DistIL has the following limitations to explore: 1. Dependence on expert quality; 2. Computational overhead; 3. Multimodal expansion (currently focused on text domains).

Summary

DistIL opens a new path for training large models using rich feedback via distributed DAgger and forward cross-entropy objectives. Its theoretical guarantees and cross-domain validation show it is worth in-depth exploration, providing a technical foundation for enhancing model capabilities.