Layer-Aligned Distillation in Deep Transformers

• Layer-aligned distillation is a method that aligns intermediate neural layers between teacher and student models to preserve semantic richness and boost performance.
• Automated mapping via genetic algorithms systematically optimizes layer correspondence, resulting in significant performance gains and near-teacher accuracy.
• Empirical evaluations on benchmarks like GLUE confirm that optimal layer mapping improves task results and doubles inference speed compared to standard methods.

Layer-aligned distillation is an advanced knowledge distillation paradigm in which the internal representations of a deep teacher neural network are aligned and transferred to a shallower or differently structured student network, not simply at the output level, but through careful, systematic mappings at various intermediate stages (or layers) of the network. This approach seeks to maximize the fidelity and semantic richness of the student's internal computations, thereby improving task performance, compression ratio, and model robustness. Layer alignment addresses the crucial question of how to associate student layers with appropriate teacher layers—an issue of particular relevance in deep Transformer architectures, such as BERT, where layers can encode distinct and hierarchical information.

1. The Importance of Layer Mapping in BERT Distillation

Layer mapping in BERT distillation refers to the function $g(m)$ which designates, for each student layer $m$, which layer of the teacher model should provide supervisory signals during training. Given a teacher with $N$ layers and a student with $M$ layers, $g(m)$ specifies which teacher layer $T_{g(m)}$ should be matched by student layer $S_m$. This mapping is critical because layers in deep transformers encode different kinds of knowledge (from syntactic to semantic), and naïve mappings—such as uniform spacing or aligning only the last layers—can lead to suboptimal transfer and degraded downstream performance.

The general form of a KD loss with layer alignment is:

$$\mathcal{L}_{KD} = \sum_{x \in \mathcal{X}} \sum_{m=1}^{M} \lambda_m \, L_{\text{layer}}(S_m(x), T_{g(m)}(x))$$

where $L_\text{layer}$ is a layer behavior-based loss (e.g., MSE on hidden states and attention maps).

Empirical evaluations show that different mappings produce significant variance in student performance, with optimal mappings delivering state-of-the-art results and poor mappings causing several percentage points drop across GLUE and similar benchmarks. For instance, in one experiment, a 4-layer student with mapping $(0,0,5,10)$ dramatically outperforms uniform or "last-layer" mapping approaches on several tasks.

2. Automated Layer Mapping via Genetic Algorithm

Given the super-linear number of possible mapping assignments ($9,375$ possible layer mappings for a 6-to-12-layer mapping), a systematic search is computationally infeasible. The genetic algorithm (GA) offers an automated solution:

• Encoding: Each candidate mapping is a gene (tuple of teacher layer indices).
• Population: Initiated with random mappings.
• Fitness Evaluation: Each mapping's fitness is determined by student model performance on a suite of proxy tasks after distillation with that mapping.
• Selection, Crossover, Mutation: Genetic operations promote exploration and exploitation in the mapping space, accelerating discovery of optimal assignments.
• Termination: The algorithm converges when no further performance improvement is observed across generations.

This automated process finds mappings that consistently outperform fixed heuristics. The effectiveness is underscored by empirical benchmarks: students distilled with GA-derived mappings not only surpass those distilled with uniform or last-layer mappings, but achieve near-parity with the teacher model while delivering 2x inference speedups.

3. Proxy Evaluation for Efficient Search

A bottleneck in mapping search is the cost of repeatedly distilling and evaluating full models. To address this, a proxy evaluation setting is introduced:

• Data Sampling: Only 10% of the full training corpus is used during the GA search phase, resulting in a 10$\times$ reduction in computational demand.
• Representative Tasks: Fitness is measured as the average score across three diverse tasks (SST-2, MNLI, SQuAD v1.1), ensuring broad transferability.
• Proxy Validity: The ranking of mappings (i.e., the best mapping found in proxy runs) strongly correlates with final full-corpus performance, justifying this efficiency measure.

This strategy makes automated, optimal layer-aligned distillation practically feasible for standard hardware budgets and large model classes.

4. Empirical Evidence and Benchmarks

Assessments are conducted using BERT-base (12-layer) as the teacher and students with 4 or 6 layers. The outcomes include:

• GLUE dev scores for 6-layer ELM student:
  • CoLA (mcc): 54.2 (better than 49.2 for MiniLM and 42.8 for TinyBERT)
  • MNLI: 84.2 (ties or beats all published baselines)
  • GLUE average: 82.9 (only 0.5 below full BERT-base)
  • 4-layer student also consistently outperforms TinyBERT and MiniLM variants.
• Speedup: ELM-based students run at approximately double the inference speed of BERT-base.
• The methodology generalizes to other languages and domains; similar SOTA results are observed in ChineseGLUE.

These results establish that layer mapping search is a major, often underappreciated, determinant of student model effectiveness across a wide range of tasks.

5. Broader Implications for Knowledge Distillation

The findings have several implications for distillation practice and research:

• Non-uniform layer alignment is essential: Uniform or "last-layer-only" mapping underuses the informational structure of deep models, causing students to neglect valuable intermediate representations.
• Selective layer supervision: Not every layer of the student must be supervised; in fact, optimal mappings sometimes avoid direct input/output layer alignment entirely, focusing instead on intermediate teacher layers.
• Transferable tools: The use of automated search and proxy settings is extensible beyond BERT, and can be plugged into contemporary KD pipelines (e.g., MiniLM, TinyBERT) to yield further gains.

Areas for future work include multi-lingual and multi-task layer alignment, jointly searching over mapping and objective space, automated student architecture co-design, and application to other transformer domains (vision, speech, multimodal).

6. Conclusion

Layer-aligned distillation, as defined and analyzed in this context, establishes that the arrangement and selection of layer correspondences in knowledge distillation is a powerful handle for model compression and student capacity utilization. The adoption of genetic algorithm-driven mapping search and efficient proxy evaluation offers a scalable, practical path to strong, compact models. The approach quantifies and systematizes a critical, previously heuristic aspect of distillation procedure, closing the performance gap with teacher models while significantly accelerating inference.