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TL;DR
A Hugging Face research post describes HOPE, a method for pruning mixture-of-experts models that considers 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; independent replication and production savings are not established in the supplied material.
Hugging Face researchers have presented HOPE, a method for pruning experts from mixture-of-experts (MoE) language models that considers how expert pairs work together, as detailed in the original analysis. The authors report that at 50% pruning, HOPE ranked best on average among the methods they compared and improved results by up to 6.1% on agentic coding tasks over the next-best pruning method. The findings could interest teams seeking to reduce model memory needs, but the supplied account does not include full evaluation details or independent confirmation.
MoE models contain multiple expert networks within each layer, while a router selects a subset to process each token. The Hugging Face post gives Qwen3.5-122B as an example: it describes 48 MoE layers, each with 256 experts, of which eight are used per layer for a given token. The researchers estimate that expert MLPs make up 95% of the model’s roughly 250 GB parameter memory footprint. That figure is an example estimate, not a reported measurement of savings from applying HOPE.
Pruning permanently removes selected experts, potentially reducing the expert weights that must be kept in memory. The researchers say HOPE needs no additional training and can be combined with quantization. Its selection method differs from approaches that score experts one at a time: it uses calibration data to estimate the cost of removing a set of experts, including terms for pairs that may be activated together, then chooses a set intended to minimize a quadratic objective.
The authors report that HOPE had the best average rank at a 50% pruning rate among the methods included in their comparison. They also report gains of up to 6.1% on agentic coding tasks compared with the next-best pruning method. The source summary does not specify the benchmark list, the exact meaning or calculation of the reported gain, or the results for each model and task, so the figure should be read as an author-reported result rather than a general performance guarantee.
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.
Potential Savings in MoE Serving
Large MoE models can require substantial memory for expert weights even though only a fraction of experts are selected for an individual token. Removing experts could lower the amount of model data operators need to keep available, addressing a practical constraint in serving models at scale. The value of pruning depends on whether the smaller model retains useful performance on the workloads it is meant to handle.
HOPE targets a particular weakness in independent expert scoring: an expert’s usefulness may depend partly on which other experts remain. If expert pairs contribute in ways that individual rankings fail to capture, accounting for those relationships may improve decisions about what to remove. The reported coding-task result is evidence from the authors’ comparison, but it does not establish that every pruned model will perform better, or that the method will deliver the same memory or speed gains in production.
For model operators, the practical question is not only whether a benchmark score holds, but also how pruning changes memory use, inference speed, and quality on their own tasks. Those outcomes can vary with the model, calibration data, pruning level, hardware, and serving setup. The supplied account does not report production measurements, so it cannot quantify the operational benefit.
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How HOPE Selects Experts
In a typical MoE layer, a router selects a set of experts for each token and combines their outputs using gate weights. Many pruning methods assign an individual importance score to each expert and remove those judged less important. The researchers’ rationale for HOPE is that this approach may miss recurring patterns in which experts are selected together.
HOPE is described as a second-order pruning method: it estimates the effect of removing a group of experts and includes pairwise interaction terms in its objective. The post says prior research has also found stable patterns of experts being selected together, but the supplied material does not identify that research or provide its findings. It also does not include a full citation, publication date, benchmark tables, or details of the comparison methods.
The method addresses a specific design choice in reducing MoE models: which experts to remove while trying to preserve performance. The researchers say their approach can be used without additional training, though applying it still involves calibration data. The available source summary does not explain the calibration requirements or provide implementation instructions.
““Experts don’t work alone.””
— Hugging Face researchers, in the research post
Evidence and Deployment Questions
The supplied source is a summary of a research post and does not provide the full paper citation, publication date, benchmark list, or underlying results tables. It is consequently not possible from this material to assess the exact evaluation design, the comparison baselines, or how the reported average rank and coding-task gain were calculated.
It is also unclear how HOPE performs across different MoE model families, calibration datasets, pruning rates, and general language tasks. The reported maximum gain does not show how frequently gains occurred or whether other benchmarks declined. No independent replication or production evaluation is described, and the example estimate for Qwen3.5-122B does not establish the memory reduction achieved by pruning.
The source says the post explains how to use HOPE, but the supplied excerpt includes no code release, implementation steps, or reproducible evaluation instructions. Until those details and broader measurements are available, the results support interest in the method rather than a conclusion that it is broadly superior.
Full Results and Reproduction
Readers seeking to test HOPE will need the full research post or accompanying paper and materials for code, calibration requirements, and reproducible instructions; these are not included in the source summary. The supplied material does not announce a specific release date or next evaluation milestone.
The most useful follow-up evidence would be full benchmark tables and tests across multiple MoE models and workloads, alongside direct measurements of memory use and inference performance after pruning. Independent replication would help establish whether the reported advantage holds beyond the authors’ comparison. Until then, teams considering the method would need to evaluate it against their own models, tasks, and deployment constraints.
Key Questions
What is HOPE?
HOPE is an expert-pruning method described by Hugging Face researchers for mixture-of-experts language models. It considers pairwise interactions between experts when choosing which ones to remove.
What results did the researchers report?
The authors report that HOPE had the best average rank among the compared methods at 50% pruning, with gains of up to 6.1% on agentic coding tasks over the next-best method. The supplied summary does not include the benchmark tables or full evaluation details.
Does HOPE require additional training?
The researchers say HOPE requires no additional training and can be combined with quantization. Their description says the method uses calibration data, but the supplied material does not give its requirements or implementation steps.
Does the reported result prove that pruning improves every model?
No. The reported result is an author-reported comparison and does not establish that every pruned model will improve. Performance and memory outcomes may depend on the model, task, calibration data, pruning rate, and deployment setup.
Primary source: Hugging Face · via ThorstenMeyerAI.com
