Principle:CarperAI Trlx Scaling Benchmarking

Knowledge Sources	Scaling Laws for LLMs
Domains	Benchmarking, Distributed_Training
Last Updated	2026-02-07 16:00 GMT

Overview

Methodology for measuring training throughput and resource efficiency across multiple model scales to validate distributed training infrastructure.

Description

Scaling benchmarks systematically measure training throughput (samples per second, tokens per second) across a range of model sizes, from small (1B parameters) to very large (66B+ parameters). By using a fixed, simple reward function (e.g., constant reward), benchmarks isolate infrastructure performance from training dynamics. This enables comparison between different distributed training backends (NeMo/Megatron vs. DeepSpeed), identification of scaling bottlenecks, and validation of hardware utilization.

Usage

Use this principle when evaluating or comparing distributed training backends. Essential before committing to a training infrastructure for a large-scale RLHF project, to ensure the chosen backend scales efficiently to the target model size.

Theoretical Basis

Key metrics:

1. Throughput: Samples processed per second at each model scale.
2. Scaling Efficiency: $\eta =\frac{T_1}{N\cdot T_N}$ where $T_1$ is single-GPU throughput and $T_N$ is N-GPU throughput.
3. Memory Efficiency: Peak GPU memory utilization vs. theoretical minimum.

Pseudo-code Logic:

# Abstract algorithm (NOT real implementation)
for model_size in ["1.3B", "6.7B", "13B", "20B", "33B", "66B"]:
    config = get_config(model_size)
    dummy_reward = lambda samples: [0.5] * len(samples)
    throughput = measure_throughput(config, dummy_reward)
    log_metrics(model_size, throughput)

Related Pages

• Implementation:CarperAI_Trlx_NeMo_Scaling_Benchmark