zen-rl

Diagnose failed RL training runs from TensorBoard logs.

No LLM. No API key. No network. Instant.

pip install zen-rl
zen runs/my_experiment

LR_TOO_HIGH
Drop learning rate to 3e-4 and add a 500 step warmup, then rerun.
  Evidence: train/approx_kl = 21.73 at step 4096

What it does

You point it at a TensorBoard log directory. It tells you what went wrong, shows you the number that proves it, and tells you what to try next.

Every claim cites a metric, a value, and a step. You can check its work.

What it detects

When it finds nothing, it says so and lists what it checked. It does not claim your run is fine — only that these five things didn't fire.

Root cause, not symptom list

A high learning rate causes entropy collapse, which kills the run. That's one problem, not three. zen-rl reports the cause and suppresses the consequences.

Scope

Built and tested against PPO / Stable-Baselines3 / TensorBoard. Other algorithms and loggers may work if the metric names match.

Thresholds were derived from 16 deliberately broken CartPole runs. They are a starting point, not gospel — different environments will need different numbers. Issues and PRs welcome, especially with a log attached.

Why no LLM

The failure modes are deterministic. approx_kl = 21.7 means the same thing every time. A model would be slower, cost money, and hallucinate. An if-statement is the right tool.

Install

pip install zen-rl

Requires Python 3.11+.

Usage

zen path/to/run          # diagnose a run
zen history              # what zen has seen before

Status

Early. 5 failure modes, 24/24 on the internal eval set, zero false positives. The eval set is synthetic — runs broken on purpose. If you have a real failed run it gets wrong, that's the most useful bug report you can file.

License

MIT