The Role of Feedback Alignment in Self-Distillation

Problem
This work addresses the underexplored area of context design in self-distillation for language models, particularly focusing on how feedback influences the learning process. The authors highlight that while conditioning on additional context typically improves model responses, the specific design of this context has not been rigorously examined. This paper is a preprint and has not undergone peer review.

Method
The authors propose a framework where a language model (the solver) is trained using feedback from a frozen critic. They compare three distinct feedback conditions: (i) a binary reward signal (GRPO), (ii) conditioning on a reference solution, and (iii) a step-by-step critique aligned with the solver’s reasoning trace. The step-aligned critique is designed to provide targeted feedback, addressing only the tokens where the model’s reasoning fails, thereby preserving correct outputs. The training process involves standard supervised learning techniques, although specific details on the architecture, loss functions, and training compute are not disclosed.

Results
The results indicate that the step-aligned critique condition yields the most significant performance improvements. Specifically, it outperforms the GRPO condition by 16.11 points and the reference-solution-conditioned self-distillation by 5.27 points on the Avg@12 metric. This suggests that the structural alignment of feedback with the solver’s reasoning is crucial for effective self-distillation. The per-token advantage analysis further elucidates that the step-aligned feedback allows the model to retain correct behaviors while only adjusting the erroneous tokens, contrasting with the reference solution approach that forces changes across all tokens.

Limitations
The authors acknowledge that their study is limited by the reliance on a frozen critic, which may not capture the dynamic nature of model learning in real-time applications. Additionally, the specific choice of benchmarks and the generalizability of the findings to other architectures or tasks are not thoroughly discussed. The paper does not explore the scalability of the proposed method or its performance in more complex reasoning tasks beyond the evaluated scenarios.

Why it matters
This research has significant implications for the design of self-distillation methods in language models, particularly in enhancing the effectiveness of feedback mechanisms. By demonstrating that structurally aligned feedback can lead to better retention of correct behaviors, this work opens avenues for future research on optimizing feedback in various machine learning contexts. The findings suggest that careful consideration of feedback design can lead to substantial performance gains, which is critical for advancing the capabilities of language models in practical applications, as published in arXiv cs.AI.