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Research Paper#Parameter-Efficient Fine-Tuning, Reinforcement Learning, Language Models🔬 ResearchAnalyzed: Jan 3, 2026 16:12

PEFT Methods for RLVR Evaluated

Published:Dec 29, 2025 03:13
1 min read
ArXiv

Analysis

This paper provides a comprehensive evaluation of Parameter-Efficient Fine-Tuning (PEFT) methods within the Reinforcement Learning with Verifiable Rewards (RLVR) framework. It addresses the lack of clarity on the optimal PEFT architecture for RLVR, a crucial area for improving language model reasoning. The study's systematic approach and empirical findings, particularly the challenges to the default use of LoRA and the identification of spectral collapse, offer valuable insights for researchers and practitioners in the field. The paper's contribution lies in its rigorous evaluation and actionable recommendations for selecting PEFT methods in RLVR.
Reference

“Structural variants like DoRA, AdaLoRA, and MiSS consistently outperform LoRA.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 26, 2025 22:02

Ditch Gemini's Synthetic Data: Creating High-Quality Function Call Data with "Sandbox" Simulations

Published:Dec 26, 2025 04:05
1 min read
Zenn LLM

Analysis

This article discusses the challenges of achieving true autonomous task completion with Function Calling in LLMs, going beyond simply enabling a model to call tools. It highlights the gap between basic tool use and complex task execution, suggesting that many practitioners only scratch the surface of Function Call implementation. The article implies that data preparation, specifically creating high-quality data, is a major hurdle. It criticizes the reliance on synthetic data like that from Gemini and advocates for using "sandbox" simulations to generate better training data for Function Calling, ultimately aiming to improve the model's ability to autonomously complete complex tasks.
Reference

“"Function Call (tool calling) is important," everyone says, but do you know that there is a huge wall between "the model can call tools" and "the model can autonomously complete complex tasks"?”

PermalinkZenn LLM
Research#Neural Networks👥 CommunityAnalyzed: Jan 10, 2026 16:12

Building Deep Neural Networks from Scratch with Zig: A New Approach

Published:Apr 25, 2023 05:18
1 min read
Hacker News

Analysis

This article discusses the practical implementation of deep learning models using the Zig programming language, offering an alternative to more established frameworks. It highlights the potential for increased control and performance by working at a lower level.
Reference

“The article likely discusses the implementation details of deep neural networks.”

PermalinkHacker News