Amazon's HOPE Prunes 50% of MoE Experts, Beats SWE-Bench by 6.1%
By Ali Al-Montaser — Senior AI Technical Editor
Amazon's HOPE method prunes 50% of Mixture-of-Experts experts using a second-order interaction matrix, achieving a best average rank of 1.58 versus 2.42 for REAP. It scores 42.3 on SWE-Bench Pro, surpassing the unpruned model's 41.8, and reduces inference memory without retraining while remaining quantization-compatible.
Executive Overview
Amazon has unveiled HOPE (Higher-Order Pruning of Experts), a second-order pruning method for Mixture-of-Experts (MoE) models, published on the official Hugging Face blog. The method replaces independent expert scoring with a pairwise interaction matrix F of size E×E, capturing how experts cooperate. At 50% pruning, HOPE achieves a best average rank of 1.58 versus 2.42 for REAP, and scores 42.3 on SWE-Bench Pro compared to 41.8 for the unpruned model. The technique reduces inference memory without retraining, remains compatible with quantization, and adds only 1–2 seconds per layer in QP solve time.
📊 Official Technical Specifications & Data Sheet
| Technical Axis | Confirmed Official Data |
|---|---|
| 💰 Pricing & Usage Cost | No commercial token pricing; the method is published free via Amazon's paper on Hugging Face. Computational cost: one calibration forward pass + QP solve in 1–2 seconds per layer. |
| 🌐 Platforms & Immediate Availability | Available immediately via the official Hugging Face blog (huggingface.co/blog/amazon/higher-order-pruning-experts) and the accompanying research paper. Applicable to Qwen3.5-122B, Qwen3.5-35B, and GLM-4.5-Air models. |
| ⚡ Performance & Speed Benchmarks | Average rank of 2.07 out of 5 methods across all conditions; 1.58 at 50% pruning versus 2.42 for REAP. Ranked first in 39% of conditions and second in 67%. Up to +6.1% improvement on agentic coding tasks. 42.3 on SWE-Bench Pro versus 41.8 for the unpruned model. |
| 🛡️ Security & Prompt Injection Resistance | The source does not mention security or prompt-injection resistance criteria; the method is a model compression technique and does not alter the operational security layer. |
| 🧠 Memory Context (Context Window) | Qwen3.5-122B: 256 experts per layer across 48 MoE layers, with 95% of the memory footprint (~250 GB) dedicated to MLP experts, activating only 8 experts per token. 50% pruning frees enough memory for longer contexts and deeper inference. |
| 🌍 Arabic Language & Regional Support | No dedicated Arabic evaluation in the source; the method is language-agnostic and works on any MoE model. Arabic quality depends on the base model (Qwen3.5 or GLM-4.5-Air) and the calibration set used. |
Deep-Dive Features & Architecture
HOPE is built on a core observation: MoE experts do not operate independently but form stable cooperative groups that are frequently activated together. First-order methods such as REAP, MAN, EAN, and Frequency assign each expert an independent importance score and cut the least important ones, ignoring that removing expert A may dismantle a cooperative group that high-importance expert B depends on. HOPE addresses this by defining an E×E matrix F, where the diagonal element F(k,k) measures the individual importance of expert k, while off-diagonal elements F(i,j) measure the pairwise cooperative contribution between experts i and j across tokens where both are activated together.
The problem is formulated as a quadratic program: P* = argmin p⊤Fp subject to p ∈ {0,1}^E and sum(p) = |P|, solved via continuous relaxation followed by binary rounding. The solve cost is 1–2 seconds per layer, negligible compared to calibration time. The team proved that the diagonal of F recovers the REAP ranking with near-perfect accuracy (Spearman ρ = 0.988), meaning any improvement from HOPE is attributed exclusively to the off-diagonal pairwise terms. Visualizing the F matrix for GLM-4.5-Air (128 experts per layer, 106B parameters) revealed a clear block structure across early, middle, and late layers.
- Second-order interaction matrix captures expert cooperation beyond independent scores.
- Retraining-free and quantization-compatible for immediate deployment.
- Single calibration pass plus QP solve in 1–2 seconds per layer.
- Language-agnostic and applicable to any MoE architecture.
Benchmark & Competitive Performance
HOPE was evaluated on three models (Qwen3.5-122B, Qwen3.5-35B, and GLM-4.5-Air) with two calibration sets (Evol-CodeAlpaca and SWE-Bench Verified trajectories), tested on Tulu3 and SWE-Bench Pro, against four first-order baselines at pruning rates from 10% to 50%. HOPE achieved the best average rank of 2.07 out of 5 methods, with 39% first-place and 67% second-place finishes. At 50% pruning, its average rank improved to 1.58 versus 2.42 for REAP. On Qwen3.5-35B-A3B at 50% pruning, HOPE scored 42.3 on SWE-Bench Pro, matching the unpruned model (41.8), while REAP dropped to 36.2, a 13% decline. The gap widens at higher pruning rates because pairwise terms become more important when more cooperative links are broken.
Industry Impact & Enterprise Adoption
HOPE offers a practical path for developers to run large MoE models on constrained infrastructure, freeing memory without retraining and remaining compatible with quantization. This is particularly relevant for enterprises deploying agentic coding systems, where SWE-Bench Pro performance is a key metric. The method's language-agnostic nature means Arabic and other regional languages benefit indirectly through the base models, such as Qwen3.5 and GLM-4.5-Air, though no Arabic-specific evaluation is provided. The immediate availability via Hugging Face and the accompanying paper lowers the barrier to adoption for research and production teams alike.
Conclusion
Amazon's HOPE represents a significant advance in MoE pruning, delivering state-of-the-art results at 50% pruning with a 1.58 average rank and 42.3 on SWE-Bench Pro, surpassing the unpruned model. By accounting for expert cooperation through a second-order interaction matrix, HOPE outperforms first-order methods like REAP and provides a retraining-free, quantization-compatible solution for memory-constrained inference. The method is available now via the official Hugging Face blog and research paper.
Media Source: Hugging Face | البيان الرسمي للشركة: المصدر الأصلي | Fact Verification & Analysis: AI Tools Oasis
Frequently Asked Questions
HOPE (Higher-Order Pruning of Experts) is a second-order method that prunes experts in Mixture-of-Experts models using an E×E interaction matrix F that captures pairwise contributions between cooperating experts, instead of scoring each expert independently. It is solved as a quadratic program (QP) in 1–2 seconds per layer after a single calibration pass.
At 50% pruning, HOPE achieved an average rank of 1.58 versus 2.42 for REAP, and scored 42.3 on SWE-Bench Pro with Qwen3.5-35B-A3B compared to 41.8 for the unpruned model, while REAP dropped to 36.2, a 13% decline. HOPE ranked first in 39% of conditions and second in 67%.
HOPE is open via a research paper and published on the Hugging Face blog with no commercial token pricing. The computational cost is one extra calibration forward pass over first-order methods, plus QP solving in 1–2 seconds per layer, which is negligible relative to calibration time.
HOPE was tested on three models: Qwen3.5-122B, Qwen3.5-35B, and GLM-4.5-Air, using two calibration sets (Evol-CodeAlpaca and SWE-Bench Verified trajectories), and compared against four first-order baselines: REAP, MAN, EAN, and Frequency, at pruning rates from 10% to 50%.
The official source does not mention an Arabic-specific evaluation. HOPE is a general pruning method that works on any MoE model regardless of language, and its quality is measured via Tulu3 and SWE-Bench Pro. Arabic support depends on the base pruned model, such as Qwen3.5 or GLM-4.5-Air.
AI Tools Oasis Team
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