- Executive Strategy for Self-Evolving Agent Skills. SkillOpt treats a compact - natural-language skill document as the trainable state of a frozen language - agent, then learns that document through rollouts, reflection, bounded edits, - and held-out validation gates. -
- - - - - - -- SkillOpt makes the skill document itself the optimization target. The - target model, backend, and harness stay fixed; the procedure that guides - evidence gathering, tool use, verification, and output formatting evolves. -
-- Instead of fine-tuning a model or hand-maintaining prompts, SkillOpt runs - the frozen agent on scored batches, asks a separate optimizer model to - propose structured edits, and accepts a candidate only when validation - performance improves. -
-The target model executes tasks with the current skill and records scored trajectories.
-The optimizer analyzes success and failure minibatches to find reusable procedures.
-Candidate add, delete, and replace operations are merged and ranked under a budget.
-The candidate skill is kept only if it improves held-out selection performance.
-- The loop deliberately mirrors a learning algorithm: rollout evidence acts - like a forward pass, reflection acts like a language-level backward pass, - and the textual learning rate bounds how far the skill can move. -
-Rollout batches capture messages, tool calls, verifier feedback, task metadata, and final scores.
-Failures and successes are reflected separately so edits correct recurring errors while preserving working behavior.
-An edit budget functions as a textual learning rate, preventing useful rules from being overwritten by broad rewrites.
-Rejected edits, slow update, and optimizer-side meta skill provide longer-horizon feedback without bloating deployment.
-
- - The table reports main-result gains across target models and - execution harnesses, comparing no-skill execution with the final - SkillOpt skill on held-out test splits. -
-| Target model | -Harness | -SearchQA | -Sheet | -Office | -DocVQA | -LiveMath | -ALFWorld | -Avg gain | -
|---|---|---|---|---|---|---|---|---|
 GPT-5.5 |
- Direct chat | -+9.6 | -+38.9 | -+39.0 | -+12.4 | -+29.3 | -+11.9 | -+23.5 | -
| GPT-5.4 |
- Direct chat | -+6.2 | -+21.1 | -+12.8 | -+13.6 | -+7.2 | -+15.6 | -+12.8 | -
| GPT-5.4-mini |
- Direct chat | -+4.3 | -+11.4 | -+26.7 | -+16.5 | -+4.8 | -+12.7 | -+12.7 | -
| GPT-5.4-nano |
- Direct chat | -+19.0 | -+8.2 | -+33.7 | -+49.4 | -+4.0 | -+35.1 | -+24.9 | -
| GPT-5.2 |
- Direct chat | -+11.2 | -+18.9 | -+21.5 | -+16.5 | -+15.2 | -+16.4 | -+16.6 | -
 Qwen3.5-4B |
- Direct chat | -+3.1 | -+14.6 | -+15.2 | -+2.1 | -+29.6 | -+50.7 | -+19.2 | -
| Qwen3.6-35B-A3B |
- Direct chat | -+7.6 | -+9.3 | -+1.2 | -+3.8 | -+10.4 | -+22.4 | -+9.1 | -
| GPT-5.5 |
- Codex | -+5.5 | -+57.5 | -+12.8 | -+5.0 | -+28.0 | -N/A | -+21.8 | -
| GPT-5.5 |
- Claude Code | -+4.0 | -+58.3 | -+13.9 | -+3.5 | -+13.3 | -N/A | -+18.6 | -
- The paper isolates the optimizer components that keep skill learning stable: - enough evidence, bounded textual updates, rejected-edit feedback, slow - update, and optimizer-side memory. -
-| Component | -Setting | -SearchQA | -Spreadsheet | -LiveMath | -
|---|---|---|---|---|
| Learning rate | -lr=4 default | -87.1 | -77.5 | -61.3 | -
| Learning rate | -without lr | -84.6 | -75.7 | -57.3 | -
| Rejected buffer | -with buffer | -87.1 | -77.5 | -61.3 | -
| Rejected buffer | -without buffer | -85.5 | -72.9 | -58.9 | -
| Update memory | -meta skill + slow update | -87.1 | -77.5 | -61.3 | -
| Update memory | -without both | -86.3 | -55.0 | -59.7 | -
- - This ALFWorld run uses GPT-5.4-mini as the frozen target model and - GPT-5.5 as the optimizer model. The plot tracks train rollout and - held-out selection scores; hover or focus a point to inspect the - skill edit proposed at that stage. -
-
- SkillOpt exports a compact best_skill.md. The paper tests
- whether that artifact transfers across model sizes, execution harnesses,
- and nearby benchmarks without further target-side optimization.
-
GPT-5.4 LiveMath skill transferred to GPT-5.4-nano on LiveMathBench.
-Codex-trained SpreadsheetBench skill transferred into Claude Code.
-GPT-5.4-nano used as its own optimizer improved SpreadsheetBench over baseline.
-The target model consumes only the final skill, not optimizer memory.
-- The final paper link and BibTeX will be updated when the preprint is available. -
-Use this placeholder until the camera-ready citation is available.
-
- @misc{skillopt2026,
- title = {SkillOpt: Executive Strategy for Self-Evolving Agent Skills},
- author = {SkillOpt Authors},
- year = {2026},
- note = {Preprint forthcoming},
- url = {https://microsoft.github.io/SkillOpt/}
-}- Executive Strategy for Self-Evolving Agent Skills. SkillOpt treats a compact - natural-language skill document as the trainable state of a frozen language - agent, then learns that document through rollouts, reflection, bounded edits, - and held-out validation gates. -
- - - - - - -- SkillOpt makes the skill document itself the optimization target. The - target model, backend, and harness stay fixed; the procedure that guides - evidence gathering, tool use, verification, and output formatting evolves. -
-- Instead of fine-tuning a model or hand-maintaining prompts, SkillOpt runs - the frozen agent on scored batches, asks a separate optimizer model to - propose structured edits, and accepts a candidate only when validation - performance improves. -
-The target model executes tasks with the current skill and records scored trajectories.
-The optimizer analyzes success and failure minibatches to find reusable procedures.
-Candidate add, delete, and replace operations are merged and ranked under a budget.
-The candidate skill is kept only if it improves held-out selection performance.
-- The loop deliberately mirrors a learning algorithm: rollout evidence acts - like a forward pass, reflection acts like a language-level backward pass, - and the textual learning rate bounds how far the skill can move. -
-Rollout batches capture messages, tool calls, verifier feedback, task metadata, and final scores.
-Failures and successes are reflected separately so edits correct recurring errors while preserving working behavior.
-An edit budget functions as a textual learning rate, preventing useful rules from being overwritten by broad rewrites.
-Rejected edits, slow update, and optimizer-side meta skill provide longer-horizon feedback without bloating deployment.
-
- - The table reports main-result gains across target models and - execution harnesses, comparing no-skill execution with the final - SkillOpt skill on held-out test splits. -
-| Target model | -Harness | -SearchQA | -Sheet | -Office | -DocVQA | -LiveMath | -ALFWorld | -Avg gain | -
|---|---|---|---|---|---|---|---|---|
| GPT-5.5 |
- Direct chat | -+9.6 | -+38.9 | -+39.0 | -+12.4 | -+29.3 | -+11.9 | -+23.5 | -
| GPT-5.4 |
- Direct chat | -+6.2 | -+21.1 | -+12.8 | -+13.6 | -+7.2 | -+15.6 | -+12.8 | -
| GPT-5.4-mini |
- Direct chat | -+4.3 | -+11.4 | -+26.7 | -+16.5 | -+4.8 | -+12.7 | -+12.7 | -
| GPT-5.4-nano |
- Direct chat | -+19.0 | -+8.2 | -+33.7 | -+49.4 | -+4.0 | -+35.1 | -+24.9 | -
| GPT-5.2 |
- Direct chat | -+11.2 | -+18.9 | -+21.5 | -+16.5 | -+15.2 | -+16.4 | -+16.6 | -
| Qwen3.5-4B |
- Direct chat | -+3.1 | -+14.6 | -+15.2 | -+2.1 | -+29.6 | -+50.7 | -+19.2 | -
| Qwen3.6-35B-A3B |
- Direct chat | -+7.6 | -+9.3 | -+1.2 | -+3.8 | -+10.4 | -+22.4 | -+9.1 | -
| GPT-5.5 |
- Codex | -+5.5 | -+57.5 | -+12.8 | -+5.0 | -+28.0 | -N/A | -+21.8 | -
| GPT-5.5 |
- Claude Code | -+4.0 | -+58.3 | -+13.9 | -+3.5 | -+13.3 | -N/A | -+18.6 | -
- The paper isolates the optimizer components that keep skill learning stable: - enough evidence, bounded textual updates, rejected-edit feedback, slow - update, and optimizer-side memory. -
-| Component | -Setting | -SearchQA | -Spreadsheet | -LiveMath | -
|---|---|---|---|---|
| Learning rate | -lr=4 default | -87.1 | -77.5 | -61.3 | -
| Learning rate | -without lr | -84.6 | -75.7 | -57.3 | -
| Rejected buffer | -with buffer | -87.1 | -77.5 | -61.3 | -
| Rejected buffer | -without buffer | -85.5 | -72.9 | -58.9 | -
| Update memory | -meta skill + slow update | -87.1 | -77.5 | -61.3 | -
| Update memory | -without both | -86.3 | -55.0 | -59.7 | -
- - This ALFWorld run uses GPT-5.4-mini as the frozen target model and - GPT-5.5 as the optimizer model. The plot tracks train rollout and - held-out selection scores; hover or focus a point to inspect the - skill edit proposed at that stage. -
-
- SkillOpt exports a compact best_skill.md. The paper tests
- whether that artifact transfers across model sizes, execution harnesses,
- and nearby benchmarks without further target-side optimization.
-
GPT-5.4 LiveMath skill transferred to GPT-5.4-nano on LiveMathBench.
-Codex-trained SpreadsheetBench skill transferred into Claude Code.
-GPT-5.4-nano used as its own optimizer improved SpreadsheetBench over baseline.
-The target model consumes only the final skill, not optimizer memory.
-- The final paper link and BibTeX will be updated when the preprint is available. -
-Use this placeholder until the camera-ready citation is available.
-@misc{skillopt2026,
- title = {SkillOpt: Executive Strategy for Self-Evolving Agent Skills},
- author = {SkillOpt Authors},
- year = {2026},
- note = {Preprint forthcoming},
- url = {https://microsoft.github.io/SkillOpt/}
-}