Principle:Alibaba ROLL Agentic Advantage Estimation

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

Overview

An advantage estimation principle tailored for multi-turn agentic RL with support for segment-based and trajectory-aware estimators.

Description

Agentic Advantage Estimation extends standard advantage computation to handle the unique structure of multi-turn trajectories. It supports six advantage estimators:

GAE: Generalized Advantage Estimation using a value function (requires critic network)
Reinforce: Standard REINFORCE returns
GRPO: Group-relative policy optimization (normalizes within groups)
GiGPO: Group-Improvement-based advantage with state-based grouping
Step-Reinforce: Step-level REINFORCE with discounted returns
Agentic-Reinforce: Segment-based REINFORCE that respects multi-turn response boundaries

The function also supports advantage whitening and clipping for training stability.

Usage

Use this principle after reward computation and before policy optimization in agentic RL pipelines. The choice of estimator significantly affects training dynamics and should match the environment structure.

Theoretical Basis

Agentic Reinforce (Segment-Based)

For multi-turn trajectories, advantages are computed per-segment (each turn is a segment):

${\hat{A}}_{s e g m e n t} = R_{s e g m e n t} - b_{s e g m e n t}$

Where the baseline is computed within groups sharing the same initial state.

Advantage Whitening

${\hat{A}}_{w h i t e n e d} = \frac{\hat{A} - μ (\hat{A})}{σ (\hat{A}) + ϵ}$

Related Pages

Implemented By

Implementation:Alibaba_ROLL_Agentic_Compute_Advantage

Related Heuristics

The following heuristics inform this principle:

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