Principle:ContextualAI HALOs Policy Sampling

Knowledge Sources	ContextualAI HALOs vLLM Documentation Efficient Memory Management for LLM Serving with PagedAttention
Domains	NLP, Inference
Last Updated	2026-02-08 03:00 GMT

Overview

A high-throughput text generation strategy that uses tensor-parallel vLLM inference to sample completions from a trained language model at scale.

Description

Policy sampling generates text completions from a trained language model given a set of prompts. This is a critical step in two workflows: online iterative alignment (where model outputs are scored and used as training data for the next round) and model evaluation (where outputs are benchmarked against reference models).

The key challenge is throughput: generating thousands of completions for iterative training or evaluation requires efficient batched inference. The HALOs framework uses vLLM's PagedAttention-based inference engine with tensor parallelism across multiple GPUs to achieve high throughput.

Sampling parameters (temperature, top-p, max tokens, stop tokens) control the diversity and length of generated text. Multiple samples per prompt can be generated to increase the quality of downstream feedback labeling.

Usage

Use policy sampling when you need to generate text from a trained model checkpoint. This is required for:

• Online iterative alignment (Step 2: generate completions for scoring)
• AlpacaEval benchmarking (Step 1: generate responses to evaluation prompts)
• Any workflow that needs model outputs for downstream processing

Theoretical Basis

Sampling from an autoregressive language model generates tokens sequentially:

$y_t\sim p_{\theta }(y_t|x,y_{<t})$

With nucleus sampling (top-p), the token distribution is truncated to the smallest set of tokens whose cumulative probability exceeds $p$, then renormalized. Temperature $\tau$ scales the logits before softmax:

$p_{\tau }(y_t|x,y_{<t})=\frac{\exp (z_t/\tau )}{\sum _v\exp (z_v/\tau )}$

Higher temperature increases diversity; lower temperature makes generation more deterministic.

Related Pages

Implemented By

• Implementation:ContextualAI_HALOs_Sample_Main

Uses Heuristic

• Heuristic:ContextualAI_HALOs_FSDP_Sampling_Workaround