skill-optimization

DMRL is a Document-Mediated Reinforcement Learning framework that optimizes skill documents for advertising recommendation using structured editing actions, policy optimization, and long-term reward prediction, demonstrating improved performance over baselines in large-scale deployment.

This paper proposes a diagnostic framework to assess whether an LLM-judge can effectively evaluate candidate skills in optimization tasks without a reference verifier, focusing on competence and discriminability metrics.

The paper introduces WER, a multi-phase framework that trains a Skill Optimizer using reinforcement learning from execution feedback to improve tool-using agents, achieving significant performance gains on benchmarks like BFCL v4 and τ2-bench.

This paper introduces Self-Supervised Skill Optimization (SSO), a framework that learns and optimizes reusable agent skills from unlabeled task instances using LLM-judged pairwise comparisons, without requiring ground-truth labels or rewards. SSO outperforms existing ground-truth-free prompt optimizers and approaches ground-truth-based methods on closed-ended benchmarks.

SkillOpt-Lite proposes a minimal viable pipeline for skill optimization in autonomous agents, achieving better and faster self-evolution by treating all components as editable code and integrating into production coding agents. It formalizes skill optimization via Zeroth-Order optimization and outperforms prior methods on benchmarks.

SkillOpt turns AI agent skill editing from manual modification into a training process, improving agent reliability without changing model weights, achieving consistent gains across benchmarks.

Microsoft released a new tool that uses an iterative approach similar to training models to optimize skills. It adjusts the markdown content each time, tests the results, and continuously advances improvements.

Microsoft open-sourced SkillOpt, a framework that treats agent skill documents as trainable artifacts, optimizing them through iterative feedback loops without modifying model weights. It is available as a Python library with support for self-evolving agent skills.

This post explains how to create an automated feedback loop for AI agents to iteratively improve their skills, using computer use and an observer skill to evaluate and update the skill code.

This article explores how AI agents can automatically write and optimize their skill files using techniques like SkillOpt from Microsoft Research, which treats skill documents as trainable state and delivers significant performance improvements. It addresses the challenge of manual skill tuning and presents frameworks like GEPA and EvoSkill as evolutionary approaches.

A roundup of three notable AI papers: SkillOpt treats skill documents as trainable parameters to optimize frozen agents; a new method compiles agentic workflows into model weights for 100x cost reduction; and AutoScientists introduces a decentralized agent team for long-running science without a central planner.

Voxyz announces a new GBrain feature that enables agents to iteratively improve skills using LLM-as-judge evaluation and an overnight optimization cycle.

A new paper formalizes skill optimization for agents by treating markdown skill files as trainable parameters, using bounded edits validated against holdout sets. The approach transfers well between models and improves performance on procedural benchmarks.

SkillOpt introduces a systematic controllable text-space optimizer that enables AI agents to train and improve their own skills (like 'work instructions') through iterative edits and validation, outperforming human-crafted and one-shot prompts across multiple benchmarks and models.

Microsoft Research introduces SkillOpt, a method that treats agent skill documents as trainable external state, using an optimizer model to make bounded edits validated by a held-out set. The approach achieves best or tied results across 52 evaluation cells and improves accuracy by over 23 points on GPT-5.5, with zero extra inference cost and transferable skills.

Introducing SkillOpt, an optimizer that treats natural-language skills as trainable external parameters instead of finetuning model weights. It uses bounded edits and validation gating to enable stable, controllable skill updates, achieving best or tied-best results across 52 settings on 6 benchmarks with 7 models.

MOCHA introduces a multi-objective optimization method for LLM agent skills, using Chebyshev scalarization and exponential annealing to handle hard platform constraints and discover Pareto-optimal variants, achieving significant improvements over existing optimizers.