Principle:Alibaba ROLL RLVR Configuration
| Knowledge Sources | |
|---|---|
| Domains | Reinforcement_Learning, Configuration |
| Last Updated | 2026-02-07 20:00 GMT |
Overview
A configuration management principle for defining and validating all hyperparameters and settings required by reinforcement learning with verifiable rewards (RLVR) training pipelines.
Description
RLVR Configuration encapsulates the complete set of parameters needed to run a multi-domain RL training pipeline. It extends standard PPO configuration with RLVR-specific settings including multi-domain reward routing, advantage estimation selection (GRPO, Reinforce++, GAE), reward normalization, KL penalty control, and distributed worker allocation. The configuration follows a hierarchical design where pipeline-level settings cascade to worker-level and strategy-level parameters.
The principle addresses the challenge of managing dozens of interrelated hyperparameters across distributed training components while ensuring consistency (e.g., matching sequence lengths across generation and training, validating worker class assignments).
Usage
Use this principle when setting up an RLVR training run that requires:
- Multi-domain training with configurable reward routing (math, code, general reasoning)
- Selection among advantage estimation algorithms (GRPO, Reinforce++, GAE)
- KL divergence penalty with adaptive or fixed coefficients
- Distributed worker allocation across actor, critic, reference, and reward clusters
- Validation and checkpointing configuration
Theoretical Basis
RLVR configuration brings together several theoretical components:
- PPO Hyperparameters: Policy gradient clipping ratio, KL penalty coefficient, advantage estimation parameters (gamma, lambda)
- Multi-Domain Routing: Domain interleave probabilities define sampling ratios across training domains, enabling curriculum-like training
- Reward Normalization: Running mean/std normalization with configurable group-level or sample-level statistics
- Advantage Estimation Selection: Choice between GAE (with value function), GRPO (group relative), or Reinforce++ (baseline subtraction)
Pseudo-code:
# Abstract configuration loading pattern
config = load_yaml("rlvr_config.yaml") # Hydra-managed YAML
config = validate_and_fill_defaults(config) # Post-init validation
config = propagate_to_workers(config) # Cascade to worker configs
Related Pages
Implemented By
Related Heuristics
The following heuristics inform this principle: