peft-rlvr-evaluation

Comprehensively evaluate 12+ parameter-efficient fine-tuning methods for RL with Verifiable Rewards (RLVR). Show DoRA/AdaLoRA outperform LoRA, SVD-based methods fail on RL, extreme reduction creates bottlenecks—providing empirical evidence that geometric-aware adapters align better with RL's off-principal update dynamics.

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Repositório: ADu2021/skillXiv
Última atividade na origem: 24 de março de 2026 às 19:42
Idioma detectado do SKILL.md: inglês
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name	peft-rlvr-evaluation
title	Evaluating Parameter Efficient Methods for RLVR
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.23165
keywords	["parameter-efficient","reinforcement-learning","adapter","peft"]
description	Comprehensively evaluate 12+ parameter-efficient fine-tuning methods for RL with Verifiable Rewards (RLVR). Show DoRA/AdaLoRA outperform LoRA, SVD-based methods fail on RL, extreme reduction creates bottlenecks—providing empirical evidence that geometric-aware adapters align better with RL's off-principal update dynamics.

Overview

Reveals LoRA's limitations in RL contexts through systematic PEFT evaluation.

Core Technique

Structural Variant Comparison:

# Standard LoRA sometimes suboptimal
methods = [LoRA, DoRA, AdaLoRA, PiSSA, VeRA, ...]

# Evaluate on RLVR (sparse binary rewards)
results = benchmark_all_methods(methods, rlvr_training)

# Finding: DoRA/AdaLoRA outperform

When to Use

Use when: RL fine-tuning, parameter efficiency critical, method selection.

References

Comprehensive PEFT method evaluation
RL-specific optimization dynamics
Geometric-aware adapter superiority

peft-rlvr-evaluation

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Mais deste repositório

Overview

Core Technique

When to Use

References

Mais deste repositório

Overview

Core Technique

When to Use

References