𓌳 REAP𓌳 the Experts: Why Pruning Prevails for One-Shot MoE Compression 📄 Paper • 💻 Code
DeepSeek-V3.2-REAP-345B-W3A16
REAP-pruned + W3A16 quantized DeepSeek-V3.2 for efficient deployment.
📋 Model Specifications
Property
Value
Base Model
DeepSeek-V3.2
Parameters
345B
Quantization
W3A16 (3-bit weights)
🔬 Calibration Dataset: Deep Dive
REAP's effectiveness depends critically on calibration data that represents the target use case. We specifically optimized for code generation, function/tool calling, and agentic workflows.
24% of mix — Real SWE-bench trajectories with tool calls, file edits, and multi-step reasoning
The Science Behind Dataset Selection
REAP Algorithm:
1. Forward pass calibration samples through model
2. Record which experts activate and their magnitudes
3. Compute saliency = router_weight × activation_norm
4. Prune lowest-saliency experts
Key Insight: Experts are TASK-SPECIFIC
├── Some experts specialize in natural language
├── Some experts specialize in code syntax
├── Some experts specialize in JSON/structured output
└── Some experts specialize in multi-turn context
If calibration lacks code → code-specialized experts appear "unused" → get pruned → model loses coding ability
Cerebras' Original Mix (from paper)
Cerebras used the same 3 datasets in their GLM-4.6 REAP experiments:
evol-codealpaca-v1 for code generation
xlam-function-calling-60k for tool calling
SWE-smith-trajectories for agentic tasks
We followed this exact recipe for reproducibility.
1@misc{lasby2025reap,
2 title = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
3 author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
4 year = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
5}
Sponsors
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