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This paper proposes Dual-Head Reasoning Distillation (DHRD) to address the reduced throughput for inference generation while maintaining the improved classification accuracy of Chain-of-Thought (CoT) prompting. DHRD is a simple training method that adds a classification head used for both training and inference, and an inference head used only for training. On seven tasks of the SuperGLUE benchmark, DHRD achieves relative gains of 0.65 and 5.47% over the pooled baseline model , with particularly significant gains on the implication/causality task. Since the inference head is disabled during testing, DHRD can perform inference at 96 and 142 times higher QPS than CoT decoding on the same backbone .
Takeaways, Limitations
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Takeaways:
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We present a novel methodology that addresses the inference throughput problem while maintaining the benefits of CoT prompting.
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Demonstrated performance improvements over existing models on the SuperGLUE benchmark (especially in implications/causality tasks).
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Achieve faster inference speeds (96-142x improvement over CoT decoding) by disabling the inference head.
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Limitations:
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Further validation is needed to determine whether the methodology presented in the paper can be generalized to other benchmarks or different types of tasks.
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Research is needed to determine how DHRD compares with other existing inference optimization techniques and whether there are any synergistic effects.
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Additional analysis is needed on weight adjustment and optimization between heads during model training.
