Endless Terminals Scaling Rl Environments For Term

Implement techniques from Endless Terminals: Scaling RL Environments for Terminal Agents. Environments are the bottleneck for self-improving agents

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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.

adu2021/skillxiv/tree/main/skills/skillxiv-v0.0.2-claude-opus-4.6/endless-terminals-scaling-rl-environments-for-term commit 75ca057f15

Frequently asked questions

npx skillmds@latest add adu2021/endless-terminals-scaling-rl-environments-for-term