rlwatch 0.5.0

Real-time GRPO/PPO training instability detection for ML teams

rlwatch

Catch broken RL training runs before they waste your GPU budget.

If you train language models with GRPO or PPO, you already know the pain: you kick off a run on 8 H100s, go to sleep, and wake up to find the policy collapsed into repeating the same token 12 hours ago. Nobody saw it. Nothing paged. The run just quietly rotted.

rlwatch is a tiny Python library that watches your training metrics in real time and pings you on Slack, Discord, email, or any HTTP endpoint the moment things start going wrong — before the run is ruined.

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The 30-second pitch

1. pip install rlwatch

2. Add two lines to your training script:

   import rlwatch
   rlwatch.attach()
   

3. Keep training. If something breaks, you get a message like:

   🚨 rlwatch CRITICAL: entropy_collapse Run: grpo_v3_exp12 | Step: 340 Policy entropy dropped from 2.8 to 0.4 over 50 steps (threshold: 1.0). Recommended action: reduce learning rate by 5× or increase KL penalty.

You open the dashboard, confirm the curve, kill the run, fix the config, and you've just saved ~30 GPU-hours.

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What it watches for

These are the most common ways GRPO/PPO runs go sideways. rlwatch runs a dedicated detector for each one on every training step.

Detector	In plain English	Default trip-wire
Entropy collapse	The model stopped exploring — it's now just repeating itself.	Entropy < 1.0 for 50 steps in a row
KL divergence explosion	The policy is running away from the reference model (usually the prelude to reward hacking).	KL > 3σ above the rolling mean
Reward hacking proxy	Rewards suddenly got weird — either way more variance than before, or split into two clusters (some samples hacked, some didn't).	Variance > 3× baseline, or Hartigan dip test p < 0.05
Advantage variance spike	The value function estimates just became unstable.	Advantage std > 3× rolling baseline
Loss NaN / Inf	The optimizer has blown up; any further updates corrupt the policy.	Loss is non-finite (one step is enough)
Gradient norm spike	Gradients exploded — usually the precursor to a loss NaN.	Grad norm > 3σ above frozen baseline
Reward mean drift	Reward mean is drifting monotonically — suspicious for slow reward hacking.	Monotone for 50 steps with magnitude > 0.1

Every detector has two severity levels (warning and critical), a configurable warmup period so it doesn't fire at step 3, and a cooldown so you don't get spammed.

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Quick start

pip install rlwatch                          # core library (~20 MB)
pip install "rlwatch[dashboard]"            # add the Streamlit dashboard
pip install "rlwatch[trl]"                  # add HuggingFace TRL deep integration
pip install "rlwatch[monitoring]"           # real Hartigan dip test for bimodal reward detection

Option A: two-line attach (easiest)

import rlwatch
rlwatch.attach()   # works for any framework — see below for the recommended TRL path

For HuggingFace TRL, the recommended path is to pass the trainer in directly:

import rlwatch
from trl import GRPOTrainer

trainer = GRPOTrainer(...)
monitor = rlwatch.attach(trainer=trainer)
trainer.train()

For veRL, OpenRLHF, or any custom loop, use Option B.

Option B: manual metric logging

import rlwatch

monitor = rlwatch.attach(framework="manual", run_id="grpo_v3_exp12")

for step in range(num_steps):
    # ... your training step ...

    monitor.log_step(
        step,
        entropy=policy_entropy,
        kl_divergence=kl,
        reward_mean=rewards.mean(),
        reward_std=rewards.std(),
        advantage_std=advantages.std(),
        loss=loss.item(),
        grad_norm=grad_norm.item(),
    )

See it fire

The repo ships with a simulated GRPO run that deliberately collapses entropy:

python examples/simulate_grpo_run.py   # run the simulation
rlwatch diagnose                         # get a retrospective report
rlwatch dashboard                        # open the live dashboard at localhost:8501

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Setting up alerts

Slack

export RLWATCH_SLACK_WEBHOOK_URL="https://hooks.slack.com/services/..."

Or put it in rlwatch.yaml:

alerts:
  slack:
    webhook_url: "https://hooks.slack.com/services/YOUR/WEBHOOK/URL"

Email

alerts:
  email:
    smtp_host: smtp.gmail.com
    to_addrs:
      - [e-mail]

Discord

export RLWATCH_DISCORD_WEBHOOK_URL="https://discord.com/api/webhooks/..."

Or in rlwatch.yaml:

alerts:
  discord:
    webhook_url: "https://discord.com/api/webhooks/..."
    mention_role_ids: ["123456789012345678"]   # @-mentions on critical only

Generic webhook

The universal escape hatch — POST a JSON body to any URL. Use this for PagerDuty's events API, an internal incident tracker, Mattermost, or anything else rlwatch doesn't have a dedicated channel for.

alerts:
  webhook:
    url: "https://your-service.example.com/rlwatch"
    headers:
      Authorization: "Bearer your-token"

Custom JSON template? See docs/alerts/webhook.md.

Console

Always on. Rich-formatted panels show up in stderr regardless of other channels.

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Configuration

Generate a starter config:

rlwatch init

This writes rlwatch.yaml with every threshold at its default. Tweak to taste.

Resolution order: defaults → YAML file → environment variables → attach() kwargs. Later values win.

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CLI reference

Command	What it does
rlwatch init	Write a starter rlwatch.yaml
rlwatch runs	List every monitored run in the local SQLite store
rlwatch diagnose [--run-id ID]	Print a retrospective report on a completed run
rlwatch dashboard	Launch the Streamlit dashboard at localhost:8501

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How it stores data

Everything lives in a single SQLite file at ./rlwatch_logs/metrics.db. Three tables: runs, metrics, alerts. WAL mode is on so the training loop writes and the dashboard reads concurrently without locking. Copy that .db file and you've copied the entire history of every run.

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Supported frameworks

• HuggingFace TRL — pass attach(trainer=trainer) for direct callback registration. See the end-to-end tutorial for a real GPT-2 + GRPO example that runs on CPU in ~5 minutes.
• veRL — auto-detected. Registers a custom tracking backend that forwards metrics to rlwatch. See docs/getting-started.
• OpenRLHF — framework="manual" + monitor.log_step(). Deep integration on the roadmap.
• Anything else — same as above. Every metric in log_step is optional; pass whatever your framework exposes.

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Docker

docker build -t rlwatch .
docker run -p 8501:8501 rlwatch

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Documentation

Full docs at varun1724.github.io/rlwatch — getting started, every detector explained in depth, alerts setup, configuration reference, the end-to-end TRL tutorial, and an FAQ.

Project direction

rlwatch is heading toward a hosted, team-oriented product. The local-first open-source library will stay free and useful on its own. See ROADMAP.md for the full plan.

Contributing & testing

rlwatch is a monitoring library — if it has bugs, it costs someone a GPU budget. The test harness is the most load-bearing part of the repo.

pip install -e ".[dev]"
pytest -v                                    # all five tiers
pytest --cov=rlwatch --cov-fail-under=90    # coverage gate (must pass to merge)

The suite is organized into five tiers (unit / property / simulation / integration / performance). See TESTING.md for the practical "how to run, write, and debug tests" guide and CLAUDE.md for the authoritative contract every PR has to meet.

License

MIT