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Mixture-of-Experts Meets Instruction Tuning - A Winning Combination for Large Language Models

2 min read

TL;DR

Sparse Mixture-of-Experts and instruction tuning combine synergistically — MoE models finetuned on FLAN-style data outperform dense models of equivalent FLOPs by more than either technique achieves independently.

Summary

Scaling dense models improves performance but at linear compute cost. Sparse Mixture-of-Experts (MoE) with top-2 routing increases parameter count without proportional FLOPs. When combined with FLAN-style instruction tuning, the gains exceed what either technique achieves independently, suggesting that MoE’s per-token specialization amplifies the generalization benefits of diverse instruction data. Chain-of-thought finetuning further improves MoE models on reasoning tasks. Results hold across multiple model scales, confirming the combination’s scaling robustness.

Key contributions

  1. Demonstrates a synergistic interaction between sparse MoE and instruction tuning that exceeds additive effects.
  2. Shows that top-2 expert routing MoE models outperform FLOPs-matched dense models on FLAN benchmarks.
  3. Establishes that chain-of-thought finetuning provides additional gains specifically for MoE models on reasoning tasks.
  4. Confirms the combination’s scaling robustness across multiple model sizes.

When to cite

  1. When justifying a sparse MoE architecture over a dense model under a fixed compute budget.
  2. When arguing that instruction tuning benefits are amplified by MoE’s conditional computation.
  3. When designing an instruction tuning mix for MoE models and considering whether to include chain-of-thought data.
  4. When evaluating models and needing to argue that FLOPs-matched comparisons are more meaningful than parameter-count comparisons.

LLM NLP Machine Learning

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