Principle:Volcengine Verl LoRA Configuration

Knowledge Sources	LoRA: Low-Rank Adaptation of Large Language Models QLoRA: Efficient Finetuning of Quantized LLMs
Domains	Parameter_Efficient_Training, Model_Architecture, Deep_Learning
Last Updated	2026-02-07 14:00 GMT

Overview

A parameter-efficient fine-tuning technique that injects trainable low-rank decomposition matrices into frozen model layers, dramatically reducing trainable parameter count and memory requirements.

Description

LoRA (Low-Rank Adaptation) freezes pre-trained model weights and adds small trainable matrices to selected layers. Instead of updating all parameters during fine-tuning, LoRA adds pairs of low-rank matrices (A and B) that approximate weight updates.

Key benefits:

• Memory efficient: Only LoRA parameters are stored in optimizer state (typically <1% of total parameters)
• Fast switching: Multiple LoRA adapters can be swapped without reloading the base model
• Composable: Adapters can be merged back into base weights for deployment

In verl, LoRA is configured through the model configuration and applied using the PEFT library. It is supported in both SFT and RL training workflows.

Usage

Use LoRA configuration when:

• GPU memory is limited for full parameter fine-tuning
• Quick experimentation with different training objectives is needed
• The pre-trained model should be preserved (no catastrophic forgetting risk)

Configure via model.lora_rank > 0 with associated lora_alpha and target_modules.

Theoretical Basis

LoRA parameterizes weight updates as low-rank decomposition:

$W_{new}=W_{frozen}+\frac{\alpha }{r}B\cdot A$

Where:

• $W_{frozen}\in {\mathbb{ℝ}}^{d\times k}$ is the pre-trained weight (frozen)
• $A\in {\mathbb{ℝ}}^{r\times k}$ and $B\in {\mathbb{ℝ}}^{d\times r}$ are trainable low-rank matrices
• $r\ll \min (d,k)$ is the rank (typical values: 8-64)
• $\alpha$ is the scaling factor (typical: 16-32)

Pseudo-code:

# Abstract LoRA application
from peft import LoraConfig, get_peft_model, TaskType

lora_config = LoraConfig(
    r=lora_rank,                    # e.g., 32
    lora_alpha=lora_alpha,          # e.g., 16
    target_modules=target_modules,  # e.g., "all-linear"
    task_type=TaskType.CAUSAL_LM,
)
model = get_peft_model(base_model, lora_config)

Related Pages

Implemented By

• Implementation:Volcengine_Verl_Get_Peft_Model_LoRA