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TL;DR
A Hugging Face research post describes HOPE, a method for pruning experts from mixture-of-experts models while accounting for pairwise interactions between experts. Its authors report the best average rank among compared methods at 50% pruning and gains of up to 6.1% on agentic coding tasks over the next-best method; the supplied material does not include full evaluation details or independent replication.
Hugging Face researchers have presented HOPE, as detailed in the original analysis, a method for removing experts from mixture-of-experts (MoE) language models that accounts for how pairs of experts work together. The authors report that at 50% pruning, HOPE had the best average rank among the methods they compared and improved results by up to 6.1% on agentic coding tasks versus the next-best pruning method. The reported findings may interest model operators trying to reduce memory requirements, but the available summary does not establish that the gains generalize across models or production workloads.
MoE models contain multiple expert networks in each layer, while a router selects a subset for each token. The research post uses Qwen3.5-122B as an example: it says the model has 48 MoE layers, each with 256 experts, 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 estimate for the cited model, not a measurement of savings from applying HOPE.
Pruning permanently removes selected experts, potentially reducing the expert weights that must remain in memory. The researchers say HOPE needs no additional training and can be combined with quantization. The method uses calibration data to estimate the cost of removing a set of experts, then selects a set to minimize a quadratic objective that includes pairwise interaction terms. The authors argue this can capture relationships missed by assigning each expert an individual importance score.
The source summary reports HOPE’s strongest comparative results at 50% pruning, including the best average rank among the methods evaluated and gains of up to 6.1% on agentic coding tasks over the next-best method. The benchmark list, baseline names, and detailed results are not included in the supplied material. The coding-task figure should be read as a result reported by the authors, not as evidence that pruning improves every task or that deployed models will run faster.
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.
Lower Memory Costs for MoE Models
Large MoE models can have substantial parameter footprints even though only a fraction of their experts are selected for an individual token. Removing experts could reduce the model weights that operators need to keep available, potentially easing hardware and serving costs. HOPE targets the choice of what to remove: if experts are useful in combination, evaluating them only one at a time could lead to a less effective pruning decision.
The reported coding results suggest that pruning quality may depend on the method and benchmark. They do not show that a pruned model will use a particular amount less memory, respond faster, or retain quality in every workload. Operators would need measurements on their own model, hardware, and tasks before drawing conclusions about deployment benefits.
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How HOPE Scores Expert Pairs
In an MoE layer, a router selects experts for each token and combines their outputs using gate weights. Many pruning approaches score experts individually. HOPE instead considers pairwise interactions between experts, based on the researchers’ view that some experts are often activated together and may have interdependent contributions.
The research post says earlier work has found stable patterns of experts being selected together, but the source material provided here does not identify that research or describe its findings. HOPE’s stated objective estimates pruning error with calibration data and includes terms for pairs of experts. That is the method’s rationale; the summary alone does not show whether the pairwise approach produces consistent gains across different model families or tasks.
“Experts don’t work alone.”
— Hugging Face researchers, in the research post
Evidence Still Needs More Detail
The available source material does not give the full paper citation, publication date, benchmark list, baseline methods, or detailed results behind the average-rank and coding-task claims. It also does not specify how the method performs across model families, calibration datasets, pruning rates other than the highlighted 50% result, or general language tasks.
The summary does not establish independent replication or production outcomes. The actual memory savings and any inference-speed changes would depend on the model and the number of experts removed. The supplied excerpt also lacks implementation steps and a release link, so it is unclear what code, calibration requirements, or reproducibility materials are available.
Full Evaluations and Deployment Tests
The next useful evidence would be the full paper or accompanying materials, including complete benchmark tables, named comparison methods, calibration procedures, and reproducible implementation details. Tests across different MoE models and pruning rates would help show whether the reported advantage extends beyond the comparisons summarized by the authors.
For deployment decisions, operators would also need direct measurements of memory use and inference performance after pruning, alongside quality evaluations on workloads that reflect their use case. Until those details are available, HOPE’s reported results are a research claim worth evaluating, rather than a demonstrated guarantee of lower operating costs or better performance in production.
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 deciding which ones to remove.
What results did the researchers report?
The authors say HOPE had the best average rank among the compared methods at 50% pruning and gained up to 6.1% on agentic coding tasks over the next-best pruning method. The supplied summary does not provide the complete benchmark results or comparison details.
Does HOPE require additional training?
The research post says the method requires no additional training and can be combined with quantization. The provided material does not include the full implementation or calibration instructions.
Does the reported result prove that pruned models are faster or cheaper to run?
No. The results summarized here do not establish specific production memory savings, speed improvements, or cost reductions. Those outcomes would need to be measured for each model, hardware setup, and workload.
What information is still missing?
The supplied material omits the full benchmark list, baseline methods, detailed evaluation results, and evidence of independent replication. It also does not provide a release link or enough information to assess how results vary across models, tasks, and pruning rates.
Primary source: Hugging Face · via ThorstenMeyerAI.com
