| name | endless-terminals-scaling-rl-environments-for-term |
| title | Endless Terminals: Scaling RL Environments for Terminal Agents |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.16443 |
| keywords | ["agent","learning","training","reinforcement","benchmark","environment","evaluation"] |
| description | Implement techniques from Endless Terminals: Scaling RL Environments for Terminal Agents. Environments are the bottleneck for self-improving agents |
Overview
This skill implements concepts from the research paper [2601.16443].
When to Use
- When you need to implement techniques described in this paper
- When working on problems that this research addresses
- When you want to understand the core concepts and methodology
When NOT to Use
- This skill provides research-level insights; production implementations may require additional engineering
- Some concepts may require significant tuning for specific use cases
- Always evaluate applicability to your specific problem domain
Key Concepts
The paper addresses: Environments are the bottleneck for self-improving agents. Current terminal benchmarks were built for evaluation, not training; reinforcement learning requires a scalable pipeline, not just a dataset. We introduce Endless Terminals, a fully autonomous pipeline that procedurally generates terminal-us...
For detailed methodology and implementation details, refer to the full paper.
