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prime-rl uses loguru for logging with a global logger pattern. Logs are written to both console and files under {output_dir}/logs/. For RL training, we recommend streaming file logs into tmux panes (as set up by tmux.sh).

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tmux helper (scripts/tmux.sh)

scripts/tmux.sh sets up a tmux session for RL runs with three panes (one per subprocess):
  • Trainer: run uv run rl ... here
  • Orchestrator: follows {output_dir}/logs/orchestrator.stdout
  • Inference: follows {output_dir}/logs/inference.stdout

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Logger Architecture

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setup_logger and get_logger

We use a singleton pattern with a module-level global logger instance (_LOGGER). 
from prime_rl.utils.logger import setup_logger, get_logger

# At entrypoint - call ONCE
logger = setup_logger("info", log_file=Path("output/logs/my.log"))

# Anywhere else in codebase
logger = get_logger()
logger.info("Hello world")
How it works:
  1. setup_logger(log_level, log_file) - Initializes the global logger exactly once:
    • Creates an isolated loguru Logger instance (not the default loguru.logger) to prevent third-party code from hijacking our logs
    • Adds a stdout handler with colorized output
    • Optionally adds a file handler (deletes existing file first)
    • Raises RuntimeError if called twice
  2. get_logger() - Returns the global logger instance:
    • Raises RuntimeError if setup_logger hasn’t been called yet
    • Safe to call from any module after initialization
  3. reset_logger() - Resets the global logger to None:
    • Used in subprocesses that inherit parent state (e.g., env workers)
    • Used in tests between test cases

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RL Log File Structure

For RL training, logs are organized by component:
ComponentLog PathDescription
RL (parent)logs/rl.logMain process that spawns subprocesses
Inferencelogs/inference.stdoutvLLM inference server stdout/stderr
Orchestratorlogs/orchestrator.logRollout generation, buffer, scheduling
Trainerlogs/trainer/rank_{N}.logTraining process (one file per GPU rank)
Env Workerslogs/env_workers/{env_name}/worker_{N}.logPer-environment worker logs (optional)

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Per-Environment Logging

Environment are run via vf.EnvServer in separate processes. By default, each environment logs to a file in its run directory under {output_dir}/run_default/train/{env_name}.log and {output_dir}/run_default/eval/{env_name}.log. The log verbosity can be controlled with orchestrator.log.vf_level. 
[orchestrator.log]
level = "debug"           # Log level for prime-rl logger
vf_level = "info"         # Log level for verifiers library

output_dir/
└── run_default/
    └── train/
        ├── {env_name_1}.log
        ├── {env_name_2}.log
        └── ...
    └── eval/
        ├── {env_name_1}.log
        ├── {env_name_2}.log
        └── ...

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Torchrun

For multi-node training with torchrun, all ranks log simultaneously. To filter to master rank only: 
uv run torchrun \
  --local-ranks-filter 0 \
  --nproc-per-node 8 \
  ...
You can also use torchrun’s native log redirection: 
uv run torchrun \
  --local-ranks-filter 0 \
  --nproc-per-node 8 \
  --log-dir outputs/torchrun \
  --redirects 3 \
  --tee 3 \
  ...
This writes to outputs/torchrun/{rdzv_id}/attempt_0/{rank}/{stdout,stderr}.log.