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TL;DR
A Hugging Face research post introduces HOPE, a method for pruning experts from mixture-of-experts models that accounts for 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 versus the next-best method. The supplied summary does not include full benchmark details or independent replication.
Hugging Face researchers have presented HOPE, a method for pruning experts from mixture-of-experts (MoE) language models that accounts for how experts work together. The authors report that at 50% pruning, HOPE achieved 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. The result could matter to teams trying to reduce the memory demands of serving large models, but the supplied account does not include the full evaluation details.
MoE models contain multiple expert networks in each layer, while a router selects a subset to process each token. The Hugging Face post gives 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 post estimates that expert multilayer perceptrons account for 95% of its roughly 250 GB parameter memory footprint.
Pruning removes selected experts from the model, potentially lowering the expert weights that need to stay in memory. The researchers say HOPE does not require additional training and can be combined with quantization. Its central difference from methods that score experts one by one is that it estimates pairwise interactions among experts, particularly those often activated together.
According to the 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 say this includes interaction terms that independent importance scores miss. They report the strongest comparative results at the tested 50% pruning rate; the source summary does not give the complete benchmark table or list all comparison methods.
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 Serving
Large MoE models can have substantial parameter footprints even though only some experts are activated for each token. Pruning targets the weights themselves, offering a possible way to reduce the memory required to keep a model available for inference. That could be relevant to operators facing hardware limits or the expense of serving large models.
HOPE’s focus is not simply removing a set number of experts, but choosing which ones to remove while accounting for their relationships. If experts are repeatedly useful in combination, treating each as independent could lead to a poorer pruning choice. The reported coding-task results suggest that the choice of method may affect performance at an aggressive pruning rate. They do not show that all pruned models will improve, or that memory and inference-speed gains will be identical in deployment.
The practical value will depend on whether the reported benchmark advantage carries over to other models and workloads, and on the memory savings measured after pruning. For model teams, the post describes a candidate technique rather than proof of a general production benefit.
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Why Expert Relationships Matter
In an MoE layer, a router selects a set of experts for each token and combines their outputs using gate weights. Common pruning approaches assign each expert an importance score and remove those deemed less important. The researchers argue that such individual scoring can overlook patterns in which experts are consistently selected together.
HOPE is designed around that concern. As described in the post, its estimate of pruning error includes terms for pairs of experts, so the decision about removing one can account for its relationship with another. The researchers refer to earlier work on stable co-selection patterns, but the supplied material does not identify the studies or detail their findings. The post’s Qwen3.5-122B example illustrates the scale of an MoE model; it does not establish that HOPE has been evaluated on that model.
“Experts don’t work alone.”
— Hugging Face researchers, in the research post
Evidence Still Needs Full Details
The supplied account does not state the research post’s publication date, provide a full paper citation, name all benchmarks and baselines, or show the results behind the average ranking and coding-task gain. It is therefore not possible from this material to assess the size and consistency of the results across individual tests.
It is also unclear how HOPE performs across different MoE model families, calibration datasets, pruning rates, and general language tasks. The reported up-to-6.1% gain is tied to agentic coding tasks and is a comparison with the next-best pruning method; it should not be read as a result across all tasks. The source summary does not establish independent replication or production outcomes, nor does it provide measured memory or inference-speed savings after pruning.
Full Results and Model Tests
Readers seeking to reproduce or apply HOPE will need the full research materials for implementation instructions, code availability, calibration requirements, and complete evaluation results; those details and a release link are not included in the supplied source. Further testing across MoE models and workloads could show whether the reported ranking holds beyond the comparisons summarized by the authors.
Deployment-focused evaluations would also need to measure memory use and inference performance after pruning, alongside task quality. Until those results are available, HOPE’s reported advantage is best treated as an author-reported finding that warrants broader evaluation, rather than a demonstrated outcome for every model or serving setup.
Key Questions
What is HOPE?
HOPE is a method for pruning experts from mixture-of-experts models. Its selection process accounts for pairwise interactions between experts rather than treating each expert’s importance as wholly independent.
What results did the researchers report?
The authors report that at 50% pruning, HOPE had the best average rank among the methods they compared. They also report gains of up to 6.1% on agentic coding tasks over the next-best pruning method.
Does HOPE require additional training?
The Hugging Face post says HOPE requires no additional training and can be combined with quantization. The supplied summary does not provide the full implementation or calibration instructions.
Do the results establish production memory savings?
No. The post describes pruning as a way to reduce the expert weights retained in memory, but the supplied material does not report measured memory or inference-speed savings from production deployments. Those outcomes can vary by model and workload.
What remains to be verified?
The full benchmark details, performance across other models and tasks, independent replication, and measured deployment effects are not provided in the supplied account. Those results would help establish how broadly the reported advantage applies.
Primary source: Hugging Face · via ThorstenMeyerAI.com
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