MOCHA: Optimizing LLM Agent Skills via Chebyshev Annealing
๐กLearn how to optimize LLM agent prompts across conflicting constraints to achieve up to 15% better performance.
โก 30-Second TL;DR
What Changed
Addresses multi-objective constraints like context window limits and instruction compaction.
Why It Matters
This research provides a more robust way to refine agent prompts, moving beyond simple weighted sums to find Pareto-optimal configurations. It is particularly useful for developers managing complex agents with strict token or formatting constraints.
What To Do Next
Implement Chebyshev scalarization in your prompt optimization pipeline instead of simple weighted scoring to better handle conflicting constraints like latency and accuracy.
Key Points
- โขAddresses multi-objective constraints like context window limits and instruction compaction.
- โขUses Chebyshev scalarization to explore non-convex regions of the Pareto front.
- โขAchieved up to 14.9% relative improvement in correctness over standard baseline optimizers.
- โขSuccessfully improved skill variants where traditional methods showed zero progress.
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Original source: ArXiv AI โ