HOPE prunes MoE experts while preserving agentic coding performance

HOPE selects Mixture-of-Experts experts with a second-order objective that models cooperation between experts instead of scoring each one independently. Across three MoE models up to 122B parameters and two calibration sets, it ranks first at 50% pruning and beats the next-best baseline most clearly on agentic coding. Reported gains reach 6.1% on SWE-bench Pro. The implementation is linked through AWS's hybrid-model-factory repository.

For self-hosting MoE models, expert pruning can reduce the weight-memory bottleneck without retraining the whole model. HOPE suggests that preserving expert combinations matters more on long agentic workloads than on short static benchmarks. Trial it only with workload-matched calibration data and remeasure latency, memory and quality together.