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research#llm📝 BlogAnalyzed: Jan 17, 2026 13:45

2025: The Year of AI Inference, Ushering in a New Era of Intelligent Tools

Published:Jan 17, 2026 13:06
1 min read
Zenn GenAI

Analysis

Get ready for a revolution! The article highlights how AI inference, spearheaded by OpenAI's 'o1' model, is poised to transform AI applications in 2025. This breakthrough will make AI-assisted search and coding more practical than ever before, paving the way for incredibly useful, tool-driven tasks.
Reference

“OpenAI released o1 and o1-mini in September 2024, starting a revolution in 'inference'...”

PermalinkZenn GenAI
Research#LLM📝 BlogAnalyzed: Jan 3, 2026 06:52

The State Of LLMs 2025: Progress, Problems, and Predictions

Published:Dec 30, 2025 12:22
1 min read
Sebastian Raschka

Analysis

This article provides a concise overview of a 2025 review of large language models. It highlights key aspects such as recent advancements (DeepSeek R1, RLVR), inference-time scaling, benchmarking, architectures, and predictions for the following year. The focus is on summarizing the state of the field.
Reference

“N/A”

PermalinkSebastian Raschka
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🔬 ResearchAnalyzed: Jan 4, 2026 09:26

Generalization of RLVR Using Causal Reasoning as a Testbed

Published:Dec 23, 2025 20:45
1 min read
ArXiv

Analysis

This article likely discusses the application of causal reasoning to improve the generalization capabilities of Reinforcement Learning with Value Representation (RLVR) models. The use of causal reasoning as a testbed suggests an evaluation of how well RLVR models can understand and utilize causal relationships within a given environment. The focus is on improving the model's ability to perform well in unseen scenarios.

Key Takeaways

    Reference

    “”

    PermalinkArXiv
    Research#llm📝 BlogAnalyzed: Dec 25, 2025 13:22

    Andrej Karpathy on Reinforcement Learning from Verifiable Rewards (RLVR)

    Published:Dec 19, 2025 23:07
    2 min read
    Simon Willison

    Analysis

    This article quotes Andrej Karpathy on the emergence of Reinforcement Learning from Verifiable Rewards (RLVR) as a significant advancement in LLMs. Karpathy suggests that training LLMs with automatically verifiable rewards, particularly in environments like math and code puzzles, leads to the spontaneous development of reasoning-like strategies. These strategies involve breaking down problems into intermediate calculations and employing various problem-solving techniques. The DeepSeek R1 paper is cited as an example. This approach represents a shift towards more verifiable and explainable AI, potentially mitigating issues of "black box" decision-making in LLMs. The focus on verifiable rewards could lead to more robust and reliable AI systems.
    Reference

    “In 2025, Reinforcement Learning from Verifiable Rewards (RLVR) emerged as the de facto new major stage to add to this mix. By training LLMs against automatically verifiable rewards across a number of environments (e.g. think math/code puzzles), the LLMs spontaneously develop strategies that look like "reasoning" to humans - they learn to break down problem solving into intermediate calculations and they learn a number of problem solving strategies for going back and forth to figure things out (see DeepSeek R1 paper for examples).”

    PermalinkSimon Willison
    Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:41

    Exploration vs Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward

    Published:Dec 18, 2025 18:59
    1 min read
    ArXiv

    Analysis

    This article likely discusses a research paper on Reinforcement Learning with Value Representation (RLVR). It focuses on the exploration-exploitation dilemma, a core challenge in RL, and proposes novel techniques using clipping, entropy regularization, and addressing spurious rewards to improve RLVR performance. The source being ArXiv suggests it's a pre-print, indicating ongoing research.
    Reference

    “The article's specific findings and methodologies would require reading the full paper. However, the title suggests a focus on improving the efficiency and robustness of RLVR algorithms.”

    PermalinkArXiv