Skip to main content
This page covers common issues you may encounter when using Lab and Hosted Training, along with their solutions.

​
Environment Issues

The verifiers library is not installed in your project.
uv add verifiers
Or if you’re installing a specific environment:
prime env install my-env
This usually means a module name collision — your environment name conflicts with an existing Python package, or the environment wasn’t installed correctly.Solutions:
  • Rename your environment to avoid conflicts
  • Reinstall: prime env install my-env
  • Check that your environment module exposes a load_environment function at the top level
The environment ID doesn’t match any published environment.
# Check the exact ID
prime env info owner/environment-name

# Install a specific version
prime env install owner/environment-name@latest
Make sure you’re using the correct owner/name format and that you have access if it’s a private environment.
The environment requires API keys that aren’t set. The error message will list which keys are missing.For local evaluation:
export OPENAI_API_KEY=sk-...
For Hosted Training, add secrets to your config:
env_file = ["secrets.env"]
Or use the CLI to manage secrets:
prime secrets             # Global secrets
prime env secrets         # Per-environment secrets

​
Training Issues

The task is too hard for the model at its current capability level. The model can’t solve any examples, so there’s no reward signal to learn from.Solutions:
  • Try a larger or more capable model
  • Use easier examples (filter your dataset or adjust environment args)
  • Increase max_tokens in [sampling] — the model may need more space to reason
  • Check your rubric implementation for bugs that might always return 0
  • Run a baseline evaluation first: prime eval run my-env -m <model> -n 20 -r 1
The task is too easy — the model already solves everything.Solutions:
  • Use harder examples or a more challenging dataset split
  • Add more demanding rubric criteria
  • Use a smaller model that has more room to improve
There could be several causes:
  • Low reward diversity: If all rollouts for an example get the same reward, there’s no contrast for the model to learn from. Increase rollouts_per_example (16–32) to get more variation.
  • Learning rate too low: Try increasing learning_rate (e.g., from 1e-4 to 3e-4).
  • Batch size too small: Larger batches provide more stable gradient estimates. Try batch_size = 512.
  • Task mismatch: The task may not be suitable for RL training. Ensure the reward function produces a meaningful gradient of scores, not just binary 0/1.
A field in your TOML config has the wrong type or an invalid value.Common causes:
  • String values not quoted: model = Qwen/Qwen3-4B → model = "Qwen/Qwen3-4B"
  • Integer where float expected or vice versa
  • Missing required sections like [sampling] or [[env]]
Double-check your config against the config reference.
The model you specified isn’t currently supported for Hosted Training.
# Check available models
prime rl models
The model list is subject to change during the beta period. See Models & Pricing for the current list.

​
CLI Issues

The CLI isn’t installed or isn’t on your PATH.
# Install or reinstall
uv tool install prime

# If already installed, upgrade
uv tool install -U prime

# Verify
prime --version   # Should be >= 0.5.15
If you installed with uv tool install and it’s still not found, make sure ~/.local/bin is in your PATH.
Your CLI session may have expired.
prime login
This opens a browser window to re-authenticate.
Check the following:
  • Your config file is valid TOML (no syntax errors)
  • The model is available: prime rl models
  • The environment ID is correct and accessible
  • You’re authenticated: prime login
  • Your CLI is up to date: uv tool install -U prime

​
Evaluation Issues

Reduce concurrency when running evaluations:
prime eval run my-env -m openai/gpt-4.1-mini -n 100 -c 8
The -c flag controls maximum concurrent requests. Lower it if you’re hitting rate limits.
Qwen3 and DeepSeek-R1 models have chat templates that automatically remove <think> tags from message history. This conflicts with ThinkParser.Solution: Use MaybeThinkParser or Parser instead of ThinkParser in your environment:
# Instead of:
parser = vf.ThinkParser(extract_fn=my_fn)

# Use:
parser = vf.MaybeThinkParser(extract_fn=my_fn)
  • Check rollouts_per_example: Low values (1–2) produce noisy results. Use at least 3–5 for reliable metrics.
  • Check num_examples: Very small sample sizes can be misleading.
  • Check sampling temperature: High temperatures produce more variation between runs.
  • Check your rubric: Make sure reward functions handle edge cases (empty responses, malformed outputs, etc.).

​
Environment Development Issues

Common causes:
  • Missing dependencies: Make sure all required packages are listed in your environment’s pyproject.toml
  • Missing secrets: API keys available locally may not be set for hosted runs. Use env_file or prime secrets.
  • Hardcoded paths: Avoid absolute file paths in your environment code
  • Network access: Some external APIs may not be reachable from the hosted environment
Start with a local evaluation using verbose output:
prime eval run my-env -m openai/gpt-4.1-mini -n 5 -r 1 -v
The -v flag enables verbose logging. You can also test your environment directly in Python:
from verifiers import load_environment

env = load_environment("my-env")
# Inspect the dataset, rubric, etc.

​
Getting Help

If your issue isn’t covered here:
  • Discord: Join the Prime Intellect Discord for community support and Q&A
  • Research Support: Fill out the research support form for hands-on assistance
  • Feedback: Use the thumbs-up/down on any docs page to let us know what’s helpful or missing