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prompt-optimization

topic4 events
papersTODAY 04:00 UTC

Paper Proposes Joint Optimization of Prompts and Training Data via Failure Signals

A new arXiv paper addresses automatic prompt optimization, a technique that normally revises prompts using task feedback while leaving the training set unchanged. The authors argue that repeatedly tuning against the same examples limits feedback to already-known weaknesses, and they propose a failure-guided approach in which prompts and training data are improved together. This co-evolution aims to surface new shortcomings rather than only correcting previously identified ones.

prompt-optimization/training-data/llm/research
papersTODAY 04:00 UTC

SkillLift: Learning Dense Rubrics from Sparse Oracles for Agent Skill Evolution

A new arXiv paper introduces SkillLift, a method for improving the reusable procedural prompts that LLM agents keep as persistent skills, which lets them adapt without retraining model weights. Rather than rewriting skill text directly from execution feedback, the approach derives dense scoring rubrics from limited, costly oracle evaluations to make skill evolution more efficient. The work targets lower evaluation cost during agent skill self-improvement.

agents/llm-agents/rubrics/arxiv
papersTODAY 04:00 UTC

MOSCOPT Method Optimizes Multiple LLM Agent Skills Together

A new arXiv paper introduces MOSCOPT, an approach that jointly optimizes collections of prompts and skills for LLM agents rather than refining a single text template. The authors argue that existing prompt and skill optimization methods miss beneficial interactions between multiple skills used by an agent. The work is a research preprint and has not yet been peer reviewed.

agents/prompt-optimization/llm/research
papersSEP 10 04:00 UTC

RobustSGPO adds search-space control to semantic-gradient prompt optimization for agent harnesses

A new arXiv preprint presents RobustSGPO, an extension of semantic-gradient prompt optimization for evolving agent harnesses with execution feedback. The method addresses an unresolved part of SGPO's local update rule—deciding which portion of the prompt to edit and what kind of change to make—by constraining the search space of candidate edits. According to the authors, specifying the requested edit at each step makes automated harness improvement more controlled.

agents/prompt-optimization/research/arxiv