Implementation:Alibaba ROLL AgenticPipeline Val

Knowledge Sources	Alibaba ROLL
Domains	Reinforcement_Learning, Evaluation, Agentic_AI
Last Updated	2026-02-07 20:00 GMT

Overview

Concrete validation evaluation method for the agentic RL pipeline provided by the Alibaba ROLL library.

Description

The AgenticPipeline.val method runs validation episodes using the validation RolloutScheduler, collects episode scores, groups them by environment tag, and computes per-tag statistics (mean, max, min). It resets the validation dataset manager before each evaluation round.

Usage

Called by the agentic pipeline at configured evaluation intervals.

Code Reference

Source Location

• Repository: Alibaba ROLL
• File: roll/pipeline/agentic/agentic_pipeline.py
• Lines: L578-612

Signature

def val(self, global_step: int) -> Dict[str, float]:
    """
    Run validation evaluation loop.

    Args:
        global_step: Current global training step

    Returns:
        Dict with validation metrics:
        - val/score/mean, val/score/max, val/score/min
        - {tag}/score/mean, {tag}/score/max, {tag}/score/min per environment
        - time/get_batch_cost_val
    """

Import

from roll.pipeline.agentic.agentic_pipeline import AgenticPipeline

I/O Contract

Inputs

Name	Type	Required	Description
global_step	int	Yes	Current training step for metric labeling

Outputs

Name	Type	Description
metrics	Dict[str, float]	Validation scores per environment and aggregate

Usage Examples

# Called within the pipeline's run() method:
if step % eval_steps == 0:
    val_metrics = pipeline.val(global_step=step)
    print(val_metrics)
    # {"val/score/mean": 0.75, "sokoban/score/mean": 0.8, "frozenlake/score/mean": 0.7}

Related Pages

Implements Principle

• Principle:Alibaba_ROLL_Agentic_Validation

Environment Dependencies

This implementation requires the following environment constraints:

• Environment:Alibaba_ROLL_Python_Runtime_Environment

Heuristics Applied

No specific heuristics apply to this implementation.