Candidate Harness Preference Learning in JIT-Agent
In JIT-Agent, candidate agent harnesses are evaluated and compared under an identical backbone model. The system is trained using preference learning to select harnesses that achieve high task rewards while simultaneously preserving or reducing latency and monetary operational costs.
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Prep Sessions
Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor
Ch.3 Adaptive Agent Harness Design - Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor
Stage I Harness Customization and Preference Learning - Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor
Learn After
Under what condition are candidate agent harnesses evaluated and compared in JIT-Agent?
In JIT-Agent, candidate agent harness selection is trained using preference learning.
Beyond achieving high task rewards, what two operational factors does JIT-Agent seek to preserve or reduce when selecting candidate harnesses?