autorl-build-training

Implements bounded, checkpointable, and observable reinforcement learning training and evaluation code.

Updated Jul 9, 2026

Search Tags:#evaluation #observability #checkpointing #training #reinforcement learning #ml engineering

One-click install

npx skills add https://github.com/Lingjie-wang/autoRL --skill autorl-build-training-lingjie-wang

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: autorl-build-training
Source: https://github.com/Lingjie-wang/autoRL/tree/main/autorl-skill-workflow-v2/skills/autorl-build-training
Command: npx skills add https://github.com/Lingjie-wang/autoRL --skill autorl-build-training-lingjie-wang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Reinforcement learning training loops are often ad-hoc, unbounded, and impossible to resume or debug after failures. This Skill generates RL training and evaluation code that is checkpointable, observable, and driven by accepted contracts, so runs can be resumed, monitored, and audited. ## Core Features & Use Cases - Checkpointable Training: Implements training loops that persist state so runs can be resumed after interruption. - Observable Pipelines: Adds logging and metrics instrumentation so training progress and evaluation results can be monitored. - Contract-Driven Implementation: Builds the training and evaluation path from accepted contracts, keeping behavior bounded and predictable. - Use Case: After defining environment and reward contracts for an RL experiment, use this Skill to generate a training loop with periodic checkpointing and metric emission, then resume the run from the last checkpoint after a crash. ## Quick Start Use $autorl-build-training to implement a bounded, checkpointable, observable RL training and evaluation path from my accepted contracts.

Frequently Asked Questions about autorl-build-training

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

FAQPage Schema

How do I make an RL training loop resumable with checkpoints?▼

Persist model weights, optimizer state, and training step counters at regular intervals during the loop. On restart, load the latest checkpoint and continue from the saved step instead of starting over.

How do I add observability to reinforcement learning training?▼

Instrument the training loop to emit metrics such as episode reward, loss values, and evaluation scores at each logging interval. Structured logs and metric streams let you monitor progress and diagnose instability.

What does contract-driven RL training implementation mean?▼

The training and evaluation code is generated from accepted contracts that define environment interfaces, reward signals, and bounds. This keeps the implementation bounded and consistent with the agreed specification.

When should I not use a bounded checkpointable training approach?▼

For quick one-off experiments or pure algorithm prototyping, full checkpointing and instrumentation may add unnecessary overhead. Use it when runs are long, costly, or must be auditable and resumable.