PARM adapts reward models to multi-stage LLM pipelines via pipeline data and direct preference optimization, improving execution rate and solving accuracy on optimization benchmarks and showing transfer to GSM8K.
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FrugalGPT learns query-specific cascades across heterogeneous LLM APIs to match or exceed top-model accuracy at far lower cost.
A single LLM rewrite of skill descriptions using false positive and negative cases matches manual optimization performance in production, with most other pipeline components adding little value.
Commander-GPT is a multi-agent routing framework that assigns sub-tasks in multimodal sarcasm detection to specialized LLMs coordinated by different commander models, reporting average F1 gains of 4.4% and 11.7% on MMSD and MMSD 2.0.
citing papers explorer
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PARM: Pipeline-Adapted Reward Model
PARM adapts reward models to multi-stage LLM pipelines via pipeline data and direct preference optimization, improving execution rate and solving accuracy on optimization benchmarks and showing transfer to GSM8K.
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FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
FrugalGPT learns query-specific cascades across heterogeneous LLM APIs to match or exceed top-model accuracy at far lower cost.
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A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization
A single LLM rewrite of skill descriptions using false positive and negative cases matches manual optimization performance in production, with most other pipeline components adding little value.
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Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection
Commander-GPT is a multi-agent routing framework that assigns sub-tasks in multimodal sarcasm detection to specialized LLMs coordinated by different commander models, reporting average F1 gains of 4.4% and 11.7% on MMSD and MMSD 2.0.