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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Repositorio: ADu2021/skillXiv
Última actividad en el origen: 24 de marzo de 2026 a las 19:42
Idioma detectado de SKILL.md: inglés
Estrellas: 6
Forks: 0

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Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.

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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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Overview

Core Technique

When to Use

References

Overview

Core Technique

When to Use

References