Principle:Alibaba ROLL LLM Response Generation

Knowledge Sources	vLLM: PagedAttention vLLM Documentation Alibaba ROLL
Domains	LLM_Inference, Reinforcement_Learning
Last Updated	2026-02-07 20:00 GMT

Overview

A high-throughput inference principle for generating multiple response completions per prompt using optimized LLM serving engines during reinforcement learning rollouts.

Description

LLM Response Generation is the rollout step in RLVR training where the current policy generates completions for a batch of prompts. This step is critical for RL training as it produces the on-policy samples that will be scored by reward models and used for policy gradient updates.

The principle uses PagedAttention-based inference engines (vLLM, SGLang) to achieve high throughput by:

• Continuous batching: Dynamically scheduling requests as slots become available
• PagedAttention: Efficient KV-cache memory management via paging
• Multiple samples per prompt: Generating num_return_sequences completions per prompt for variance reduction in advantage estimation (especially for GRPO)

Usage

Use this principle during the rollout phase of any RL training pipeline that requires generating model completions. The generation strategy (vLLM vs SGLang) is configurable via the inference cluster's strategy settings.

Theoretical Basis

The generation process produces on-policy samples for policy gradient methods:

${\pi }_{\theta }(a|s)\text{ sampled via autoregressive generation with temperature }T$

For GRPO, multiple samples per prompt enable group-relative advantage estimation:

${\hat{A}}_i=\frac{r_i-{\mu }_{\text{group}}}{{\sigma }_{\text{group}}}$

Key generation parameters:

• temperature: Controls exploration (higher = more diverse)
• top_p: Nucleus sampling threshold
• max_new_tokens: Maximum completion length
• num_return_sequences: Samples per prompt for variance reduction

Related Pages

Implemented By

• Implementation:Alibaba_ROLL_VllmStrategy_Generate

Related Heuristics

The following heuristics inform this principle:

• Heuristic:Alibaba_ROLL_Dynamic_Batching_Token_Limits