exploratory-iteration

Implement autocurriculum learning with dynamic task space expansion and selective sampling.

Updated Jun 16, 2026

Search Tags:#self-improvement #iterative learning #autocurriculum #task space expansion #self-divergence

Ready to run

npx skills add https://github.com/breakingcircuits1337/agent-skills --skill exploratory-iteration

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: exploratory-iteration
Source: https://github.com/breakingcircuits1337/agent-skills/tree/main/exploratory-iteration
Command: npx skills add https://github.com/breakingcircuits1337/agent-skills --skill exploratory-iteration

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of implementing autocurriculum learning, enabling multi-step self-improvement without fixed depth.

Core Features & Use Cases

Dynamic Task Space Expansion: Grows task space from intermediate states for continuous improvement.
Selective Sampling: Selects most informative partial histories for continuation.
Self-Divergence: Supports diverse approaches to problem-solving.
Use Case: Ideal for tasks requiring multiple revision attempts, such as math problems or ML engineering tasks.

Quick Start

Use the exploratory-iteration skill to trigger an ExIt process for self-improvement in your task.

Frequently Asked Questions about exploratory-iteration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How does autocurriculum learning work for multi-step problem solving?▼

Autocurriculum learning enables multi-step self-improvement by dynamically expanding the task space from intermediate states and selectively sampling the most informative partial histories for continuation. This allows iterative improvement without fixed depth.

How do I implement dynamic task space expansion for iterative learning?▼

You can trigger dynamic task space expansion by applying the exploratory-iteration process to your task. It grows the task space from intermediate states, enabling continuous improvement and diverse approaches through self-divergence.

Can I use selective sampling to improve multi-step math problem revision attempts?▼

Yes, selective sampling is ideal for tasks requiring multiple revision attempts, such as math problems or ML engineering tasks. It selects the most informative partial histories to continue the problem-solving process.

What is the best way to enable self-divergence for diverse approaches in machine learning engineering?▼

To enable self-divergence, use an autocurriculum learning approach that supports dynamic adaptation. This allows the system to diverge into new approaches dynamically, fostering diverse problem-solving strategies within ML engineering tasks.

Does iterative learning without fixed depth require any external dependencies?▼

No, multi-step self-improvement without fixed depth does not require external dependencies. The process operates independently to dynamically adapt and expand the task space during problem-solving.

When should I use autocurriculum learning over standard fixed-depth problem solving?▼

Use autocurriculum learning when your tasks require multiple revision attempts and continuous improvement. It is necessary for multi-step scenarios where the system must dynamically adapt and diverge to new approaches rather than following a fixed path.