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research #llm 📝 BlogAnalyzed: Jan 14, 2026 07:30

Supervised Fine-Tuning (SFT) Explained: A Foundational Guide for LLMs

Published:Jan 14, 2026 03:41

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1 min read

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Zenn LLM

Analysis

This article targets a critical knowledge gap: the foundational understanding of SFT, a crucial step in LLM development. While the provided snippet is limited, the promise of an accessible, engineering-focused explanation avoids technical jargon, offering a practical introduction for those new to the field.

Key Takeaways

•SFT is a core technique in LLM fine-tuning.
•The article aims to provide an intuitive understanding from an engineering perspective.
•It frames SFT within the context of the LLM development lifecycle.

Reference

“In modern LLM development, Pre-training, SFT, and RLHF are the "three sacred treasures."”

Permalink Zenn LLM

research #llm 📝 BlogAnalyzed: Jan 10, 2026 05:00

Strategic Transition from SFT to RL in LLM Development: A Performance-Driven Approach

Published:Jan 9, 2026 09:21

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1 min read

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Zenn LLM

Analysis

This article addresses a crucial aspect of LLM development: the transition from supervised fine-tuning (SFT) to reinforcement learning (RL). It emphasizes the importance of performance signals and task objectives in making this decision, moving away from intuition-based approaches. The practical focus on defining clear criteria for this transition adds significant value for practitioners.

Key Takeaways

•The transition from SFT to RL in LLM development should be driven by performance signals and task objectives.
•SFT is responsible for teaching the LLM the format and inference rules.
•RL focuses on teaching the LLM preferences, safety, and overall quality of responses.

Reference

“SFT: Phase for teaching 'etiquette (format/inference rules)'; RL: Phase for teaching 'preferences (good/bad/safety)'”

Permalink Zenn LLM

Research Paper #Large Language Models, Agentic AI, Spatio-Temporal Reasoning 🔬 ResearchAnalyzed: Jan 3, 2026 06:18

STAgent: Agentic LLM for Spatio-Temporal Tasks

Published:Dec 31, 2025 16:39

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1 min read

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ArXiv

Analysis

This paper introduces STAgent, a specialized large language model designed for spatio-temporal understanding and complex task solving, such as itinerary planning. The key contributions are a stable tool environment, a hierarchical data curation framework, and a cascaded training recipe. The paper's significance lies in its approach to agentic LLMs, particularly in the context of spatio-temporal reasoning, and its potential for practical applications like travel planning. The use of a cascaded training recipe, starting with SFT and progressing to RL, is a notable methodological contribution.

Key Takeaways

•STAgent is a specialized LLM for spatio-temporal tasks.
•Key contributions include a stable tool environment, hierarchical data curation, and a cascaded training recipe.
•The model demonstrates promising performance on TravelBench while maintaining general capabilities.
•The approach highlights the potential of agentic LLMs for complex reasoning and practical applications.

Reference

“STAgent effectively preserves its general capabilities.”

Permalink ArXiv

Research Paper #Computer Vision, Image Segmentation, Zero-Shot Learning, Evolutionary Algorithms 🔬 ResearchAnalyzed: Jan 3, 2026 08:47

Evolving Prompts for Zero-Shot Reasoning Segmentation

Published:Dec 31, 2025 08:10

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1 min read

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ArXiv

Analysis

This paper introduces EVOL-SAM3, a novel zero-shot framework for reasoning segmentation. It addresses the limitations of existing methods by using an evolutionary search process to refine prompts at inference time. This approach avoids the drawbacks of supervised fine-tuning and reinforcement learning, offering a promising alternative for complex image segmentation tasks.

Key Takeaways

•Proposes EVOL-SAM3, a zero-shot framework for reasoning segmentation.
•Uses an evolutionary search process to refine prompts at inference time.
•Outperforms static baselines and even fully supervised methods on the ReasonSeg benchmark in a zero-shot setting.
•Addresses limitations of SFT and RL approaches.

Reference

“EVOL-SAM3 not only substantially outperforms static baselines but also significantly surpasses fully supervised state-of-the-art methods on the challenging ReasonSeg benchmark in a zero-shot setting.”

Permalink ArXiv

Research Paper #Computer Vision, Remote Sensing, Visual Question Answering, Reinforcement Learning 🔬 ResearchAnalyzed: Jan 3, 2026 08:54

Improving CDVQA with Decision-Ambiguity-guided Reinforcement Fine-Tuning

Published:Dec 31, 2025 03:28

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of decision ambiguity in Change Detection Visual Question Answering (CDVQA), where models struggle to distinguish between the correct answer and strong distractors. The authors propose a novel reinforcement learning framework, DARFT, to specifically address this issue by focusing on Decision-Ambiguous Samples (DAS). This is a valuable contribution because it moves beyond simply improving overall accuracy and targets a specific failure mode, potentially leading to more robust and reliable CDVQA models, especially in few-shot settings.

Key Takeaways

•Addresses the problem of decision ambiguity in CDVQA.
•Proposes DARFT, a reinforcement learning framework to improve discriminability.
•Focuses on Decision-Ambiguous Samples (DAS).
•Demonstrates consistent gains over SFT baselines, especially in few-shot settings.

Reference

“DARFT suppresses strong distractors and sharpens decision boundaries without additional supervision.”

Permalink ArXiv

Research Paper #Large Language Models (LLMs) in Finance 🔬 ResearchAnalyzed: Jan 3, 2026 15:39

QianfanHuijin: Multi-Stage Training for Financial LLMs

Published:Dec 30, 2025 16:10

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1 min read

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ArXiv

Analysis

This paper introduces QianfanHuijin, a financial domain LLM, and a novel multi-stage training paradigm. It addresses the need for LLMs with both domain knowledge and advanced reasoning/agentic capabilities, moving beyond simple knowledge enhancement. The multi-stage approach, including Continual Pre-training, Financial SFT, Reasoning RL, and Agentic RL, is a significant contribution. The paper's focus on real-world business scenarios and the validation through benchmarks and ablation studies suggest a practical and impactful approach to industrial LLM development.

Key Takeaways

•Introduces QianfanHuijin, a financial domain LLM.
•Proposes a multi-stage training paradigm for industrial LLM enhancement.
•Employs Continual Pre-training, Financial SFT, Reasoning RL, and Agentic RL.
•Demonstrates superior performance on financial benchmarks.
•Ablation studies validate the effectiveness of Reasoning and Agentic RL stages.

Reference

“The paper highlights that the targeted Reasoning RL and Agentic RL stages yield significant gains in their respective capabilities.”

Permalink ArXiv

Research Paper #Large Language Models (LLMs), Generalization, Reasoning, Fine-tuning 🔬 ResearchAnalyzed: Jan 3, 2026 16:50

LLM Generalization: Fine-Grained Analysis of Reasoning

Published:Dec 30, 2025 08:16

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1 min read

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ArXiv

Analysis

This paper addresses the critical issue of why different fine-tuning methods (SFT vs. RL) lead to divergent generalization behaviors in LLMs. It moves beyond simple accuracy metrics by introducing a novel benchmark that decomposes reasoning into core cognitive skills. This allows for a more granular understanding of how these skills emerge, transfer, and degrade during training. The study's focus on low-level statistical patterns further enhances the analysis, providing valuable insights into the mechanisms behind LLM generalization and offering guidance for designing more effective training strategies.

Key Takeaways

•Introduces a novel benchmark for fine-grained analysis of LLM reasoning.
•Compares SFT and RL tuning methods, revealing differences in generalization.
•Highlights the importance of understanding core cognitive skills in LLMs.
•Provides insights into designing training strategies for robust generalization.

Reference

“RL-tuned models maintain more stable behavioral profiles and resist collapse in reasoning skills, whereas SFT models exhibit sharper drift and overfit to surface patterns.”

Permalink ArXiv

Tutorial #machine learning 📝 BlogAnalyzed: Dec 24, 2025 22:17

Experiences Getting Stuck with Training Hub

Published:Dec 24, 2025 22:09

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1 min read

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Qiita AI

Analysis

This article discusses the author's difficulties in getting a runnable sample working with Training Hub, likely within the context of the SDG Hub and synthetic data generation. The author mentions using GCP (GCE) and a GPU, suggesting a focus on machine learning or AI model training. The core issue seems to stem from a lack of knowledge, prompting the author to document their experiences. The article likely provides practical insights and troubleshooting steps for others facing similar challenges when setting up and using Training Hub for AI/ML projects, especially those involving synthetic data.

Key Takeaways

•Training Hub can be challenging to set up and use, especially for beginners.
•Synthetic data generation is a potential use case for Training Hub.
•GCP (GCE) and GPUs are commonly used environments for Training Hub.

Reference

“I'm thinking of trying OSFT in Training Hub because it seems like I can create synthetic data with SDG Hub. But I had trouble getting a Runnable sample to work.”

Permalink Qiita AI

Research #Tokenization 🔬 ResearchAnalyzed: Jan 10, 2026 09:53

SFTok: Enhancing Discrete Tokenizer Performance

Published:Dec 18, 2025 18:59

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1 min read

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ArXiv

Analysis

This research paper, originating from ArXiv, likely investigates novel methods to improve the efficiency and accuracy of discrete tokenizers, a crucial component in many AI models. The significance hinges on the potential for wider adoption and performance gains across various natural language processing tasks.

Key Takeaways

•Addresses performance limitations of discrete tokenizers.
•Presents a potential advancement in tokenization techniques.
•Could impact the performance of downstream NLP tasks.

Reference

“The research focuses on discrete tokenizers, suggesting a potential improvement over existing methods.”

Permalink ArXiv

Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 13:37

Reinforcement Learning Improves Safety and Reasoning in Large Language Models

Published:Dec 1, 2025 16:35

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1 min read

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ArXiv

Analysis

This ArXiv article explores the use of Reinforcement Learning (RL) techniques to improve the safety and reasoning capabilities of Large Language Models (LLMs), moving beyond traditional Supervised Fine-tuning (SFT) approaches. The research potentially offers advancements in building more reliable and trustworthy AI systems.

Key Takeaways

•Explores using Reinforcement Learning to improve LLM reasoning.
•Aims to enhance the safety aspects of large reasoning models.
•Suggests a departure from solely Supervised Fine-tuning methods.

Reference

“The research focuses on the application of Reinforcement Learning methods.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 06:06

From Prompts to Policies: How RL Builds Better AI Agents with Mahesh Sathiamoorthy - #731

Published:May 13, 2025 22:10

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1 min read

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Practical AI

Analysis

This article from Practical AI discusses how Reinforcement Learning (RL) is being used to improve AI agents built on foundation models. It features an interview with Mahesh Sathiamoorthy, CEO of Bespoke Labs, focusing on the advantages of RL over prompting, particularly in multi-step tool use. The discussion covers data curation, evaluation, and error analysis, highlighting the limitations of supervised fine-tuning (SFT). The article also mentions Bespoke Labs' open-source libraries like Curator, and models like MiniCheck and MiniChart. The core message is that RL offers a more robust approach to building AI agents.

Key Takeaways

•Reinforcement Learning (RL) is presented as a superior method for building AI agents compared to prompting.
•Data curation, evaluation, and error analysis are crucial for improving model performance in RL.
•The article highlights the limitations of Supervised Fine-Tuning (SFT) for tool-augmented reasoning tasks.

Reference

“Mahesh highlights the crucial role of data curation, evaluation, and error analysis in model performance, and explains why RL offers a more robust alternative to prompting, and how it can improve multi-step tool use capabilities.”

Permalink Practical AI

Supervised Fine-Tuning (SFT) Explained: A Foundational Guide for LLMs

Analysis

Key Takeaways

Strategic Transition from SFT to RL in LLM Development: A Performance-Driven Approach

Analysis

Key Takeaways

STAgent: Agentic LLM for Spatio-Temporal Tasks

Analysis

Key Takeaways

Evolving Prompts for Zero-Shot Reasoning Segmentation

Analysis

Key Takeaways

Improving CDVQA with Decision-Ambiguity-guided Reinforcement Fine-Tuning

Analysis

Key Takeaways

QianfanHuijin: Multi-Stage Training for Financial LLMs

Analysis

Key Takeaways

LLM Generalization: Fine-Grained Analysis of Reasoning

Analysis

Key Takeaways

Experiences Getting Stuck with Training Hub

Analysis

Key Takeaways

SFTok: Enhancing Discrete Tokenizer Performance

Analysis

Key Takeaways

Reinforcement Learning Improves Safety and Reasoning in Large Language Models

Analysis

Key Takeaways

From Prompts to Policies: How RL Builds Better AI Agents with Mahesh Sathiamoorthy - #731

Analysis

Key Takeaways

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