🔍 Read the full analysis: Your Experts Have Partners. There's HOPE For Keeping Them Together on ThorstenMeyerAI.com
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TL;DR
A Hugging Face research post describes HOPE, a method for pruning experts from mixture-of-experts language models while accounting for pairwise interactions among experts. Its authors report the best average rank among methods compared at 50% pruning and gains of up to 6.1% on agentic coding tasks over the next-best method; independent replication and production savings are not established by the supplied material.
Hugging Face researchers have presented HOPE, a method for removing experts from mixture-of-experts language models that takes account of how experts work together. The authors report that at 50% pruning, HOPE had the best average rank among the methods they compared, with gains of up to 6.1% on agentic coding tasks over the next-best pruning method.
Mixture-of-experts, or MoE, models contain multiple expert networks in each layer, while a router selects only some of them for each token. Rather than simply scoring experts one at a time, HOPE estimates interactions between pairs that are often activated together. The researchers say this lets the method account for effects that individual importance scores can miss when deciding which experts to remove.
According to the research post, HOPE uses calibration data to estimate the cost of removing a set of experts, then selects a set by minimizing a quadratic objective. The researchers describe the method as requiring no additional training and say it can be combined with quantization. Pruning permanently deletes selected experts, unlike a strategy that dynamically loads and unloads them.
The post uses Qwen3.5-122B to illustrate the possible memory pressure: it says the model has 48 MoE layers, with 256 experts in each, and uses eight experts per layer for a given token. The authors estimate that expert MLPs account for 95% of its roughly 250 GB parameter memory footprint. That figure is an example cited in the post, not a measurement of savings achieved by HOPE.
Pruning MoE experts while keeping pairs in view
HOPE chooses experts for removal by accounting for how pairs work together. Hugging Face researchers report its strongest comparative results at 50% pruning.
How HOPE chooses
Common methods score experts one by one; HOPE includes pairwise interactions in its pruning estimate.
Estimate the cost of removing candidate experts.
Include interaction terms for experts that may work together.
Minimize the resulting quadratic estimate of pruning error.
Why memory can change
MoE routers activate only a subset of experts per token, while the model’s expert weights still need to be stored.
Qwen3.5-122B
The post describes 48 MoE layers, each with 256 experts; eight are used per layer for a token.
Delete selected experts
Permanent removal can reduce weights kept in memory. HOPE requires no additional training and can be combined with quantization.
What the reported results show
The authors compare HOPE with other pruning methods and report its strongest results at a 50% pruning rate.
- Best average rank among the methods compared at 50% pruning.
- Up to 6.1% gain on agentic coding tasks over the next-best method.
- How results vary across models, tasks, and pruning rates.
- Production memory and inference performance outcomes.
Why Expert Pruning Could Matter
Large MoE models can require substantial memory for expert weights even though only a subset of experts is used for each token. Removing experts could reduce the weights operators need to keep in memory, potentially making some models easier or less costly to serve. HOPE focuses on the selection problem: which experts can be removed while limiting performance loss.
The authors’ results suggest that the quality of pruning may depend on how the method treats relationships among experts, particularly at an aggressive 50% pruning rate. The reported coding-task advantage is potentially relevant to teams deploying models for agentic coding, but it does not show that every pruned model will improve, or that the same result will hold on other workloads.
Operational gains remain a separate question. The source material does not report measured production latency, hardware requirements, or realized memory savings after pruning. Actual effects will depend on the model, the number of experts removed, and deployment conditions. The reported benchmark results therefore speak to comparative task performance, not a guaranteed reduction in serving costs.
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How HOPE Scores Expert Sets
In an MoE layer, a router selects a group of experts for each token and combines their outputs using gate weights. A conventional pruning approach can assign each expert an individual importance score. The Hugging Face post argues that this can overlook stable co-selection patterns: particular experts may commonly be active together, so removing one could change the value of keeping another.
HOPE is described as a second-order approach because its estimate includes pairwise terms, rather than treating every expert’s contribution as independent. The researchers say earlier work has also identified stable patterns of experts being selected together, but the supplied material does not name that research or detail its findings.
The post’s reported comparison is strongest at the stated 50% pruning rate. It does not provide, in the material available here, the full benchmark table, the list of baseline methods, or results across other pruning levels. Those details are needed to judge how broadly the ranking and coding-task result apply.
“Experts don’t work alone.”
— Hugging Face researchers, in the research post
Evidence Still Needs Detail
The supplied source does not give the research post’s publication date, a full paper citation, or the underlying evaluation details. It is therefore not possible from this material to verify the average-rank comparison, interpret the exact calculation behind the reported up-to-6.1% gain, or assess which benchmark tasks and baselines were included.
It is also unclear how HOPE performs across different MoE model families, calibration datasets, general language tasks, and pruning rates below or above 50%. The source summarizes results reported by the method’s authors; it does not establish independent replication or demonstrate production outcomes. The example memory estimate for Qwen3.5-122B should not be read as a measured saving from applying HOPE.
The supplied material also does not include an implementation link or instructions for reproducing the method. Without code, calibration requirements, and detailed measurements, operators cannot determine from this summary alone whether the reported performance transfers to their own models and workloads.
Full Results and Reproduction
The next useful evidence would be the full paper or accompanying research materials, including the complete benchmark results, comparison methods, calibration setup, and implementation details. Reproducible tests across multiple MoE models and pruning rates could clarify whether HOPE’s reported advantage extends beyond the summarized comparison.
Deployment evaluations would also need to measure memory use and inference performance after pruning, rather than infer operational savings from parameter counts alone. Until those details are available, the findings are best treated as author-reported comparative results that warrant further testing, not as a general guarantee of better model performance or lower serving costs.
Key Questions
What is HOPE?
HOPE is a method proposed by Hugging Face researchers for pruning experts from mixture-of-experts models. It considers pairwise interactions between experts when selecting which ones to remove.
What results did the researchers report?
The authors report that at 50% pruning, HOPE had the best average rank among the methods compared and achieved gains of up to 6.1% on agentic coding tasks over the next-best pruning method. The supplied material does not include the full benchmark results or comparison details.
Does HOPE require additional training?
The research post says HOPE requires no additional training and can be combined with quantization. The supplied source does not provide implementation steps or enough detail to verify requirements for a particular model.
Does HOPE guarantee lower memory use or faster inference?
No such guarantee is established in the supplied material. Pruning removes experts and could reduce the weights kept in memory, but the post excerpt does not report measured production memory savings or inference-speed results. Outcomes may vary by model and deployment.
Can the reported results be independently checked?
The material provided here does not include a full citation, benchmark tables, code, or reproduction instructions. Independent assessment would require those details and tests across models and workloads.
Primary source: Hugging Face · via ThorstenMeyerAI.com
