✨On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models

📝 Summary:
This paper theoretically analyzes entropy dynamics in reinforcement fine-tuning of large language models. It derives expressions for entropy change and proposes novel entropy control methods based on discriminant analysis, aiming to optimize the exploration-exploitation balance during LLM fine-tu...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03392
• PDF: https://arxiv.org/pdf/2602.03392
• Github: https://github.com/agentscope-ai/Trinity-RFT

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#LLM #ReinforcementLearning #Entropy #AIResearch #MachineLearning

✨Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training

📝 Summary:
TRIT framework improves multilingual long reasoning by jointly training translation and reasoning. This self-improving method enhances non-English question understanding and response generation without extra data. It boosts accuracy and language consistency, also improving cross-lingual question ...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05940
• PDF: https://arxiv.org/pdf/2602.05940

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#MultilingualAI #LongReasoning #LLM #NLP #AIResearch

✨PlanViz: Evaluating Planning-Oriented Image Generation and Editing for Computer-Use Tasks

📝 Summary:
PlanViz is a new benchmark evaluating unified multimodal models for image generation and editing in computer-use planning tasks. It features route planning, work diagramming, and web&UI displaying sub-tasks, using a task-adaptive PlanScore to assess correctness, visual quality, and efficiency.

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06663
• PDF: https://arxiv.org/pdf/2602.06663
• Project Page: https://github.com/lijunxian111/PlanViz
• Github: https://github.com/lijunxian111/PlanViz/releases/tag/v1

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#MultimodalAI #ImageGeneration #ImageEditing #ComputerVision #Benchmarking

✨POINTS-GUI-G: GUI-Grounding Journey

📝 Summary:
GUI agents for automated digital tasks rely on vision-language models with enhanced grounding capabilities, achieved through refined data engineering, improved training strategies, and reinforcement l...

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06391
• PDF: https://arxiv.org/pdf/2602.06391
• Github: https://github.com/Tencent/POINTS-GUI

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨EgoAVU: Egocentric Audio-Visual Understanding

📝 Summary:
MLLMs struggle with egocentric video's joint audio-visual understanding. EgoAVU, a new data engine, generates diverse audio-visual narrations to create the EgoAVU-Instruct dataset. This fine-tunes MLLMs, enabling up to 113% performance improvement in joint audio-visual comprehension.

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06139
• PDF: https://arxiv.org/pdf/2602.06139

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#EgocentricAI #MultimodalAI #AudioVisualAI #DeepLearning #Datasets

✨DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos

📝 Summary:
DreamDojo is a foundation world model trained on 44k hours of egocentric human videos that enables efficient simulation of dexterous robotic tasks through continuous latent actions and real-time disti...

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06949
• PDF: https://arxiv.org/pdf/2602.06949
• Project Page: https://dreamdojo-world.github.io/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Back to Basics: Revisiting Exploration in Reinforcement Learning for LLM Reasoning via Generative Probabilities

📝 Summary:
ProGRPO is a novel RL method for LLM reasoning that tackles entropy collapse. It dynamically re-weights rewards to equilibrate confidence across correct responses, enhancing generative diversity and exploration. ProGRPO significantly outperforms standard methods on reasoning benchmarks.

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05281
• PDF: https://arxiv.org/pdf/2602.05281

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#ReinforcementLearning #LLM #AI #GenerativeAI #MachineLearning

✨Exploring Knowledge Purification in Multi-Teacher Knowledge Distillation for LLMs

📝 Summary:
This paper introduces Knowledge Purification, consolidating multi-teacher LLM rationales to reduce conflicts and improve distillation efficiency. Methods improve model performance and reduce conflicts; router-based methods generalize robustly.

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01064
• PDF: https://arxiv.org/pdf/2602.01064

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#LLM #KnowledgeDistillation #KnowledgePurification #AI #DeepLearning

✨Judging What We Cannot Solve: A Consequence-Based Approach for Oracle-Free Evaluation of Research-Level Math

📝 Summary:
Consequence-Based Utility evaluates math solutions by testing their value as in-context exemplars for related problems. This oracle-free approach outperforms reward models and LLM judges, improving ranking quality and correct-wrong separation of AI-generated solutions.

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06291
• PDF: https://arxiv.org/pdf/2602.06291

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#AIEvaluation #LLMEvaluation #MathAI #ArtificialIntelligence #MachineLearning

✨MSign: An Optimizer Preventing Training Instability in Large Language Models via Stable Rank Restoration

📝 Summary:
LLM training instability is linked to weight matrix stable rank decline and Jacobian alignment, causing gradient explosions. MSign is a new optimizer that restores stable rank via matrix sign operations, effectively preventing training failures with low computational overhead.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01734
• PDF: https://arxiv.org/pdf/2602.01734

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making

📝 Summary:
Baichuan-M3 is a medical LLM for clinical decision support. It uses proactive info gathering, long-horizon reasoning, and hallucination suppression. It outperforms GPT-5.2 on medical benchmarks in clinical inquiry and safety.

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06570
• PDF: https://arxiv.org/pdf/2602.06570
• Github: https://github.com/baichuan-inc/Baichuan-M3-235B

🔹 Models citing this paper:
• https://huggingface.co/baichuan-inc/Baichuan-M3-235B
• https://huggingface.co/baichuan-inc/Baichuan-M3-235B-GPTQ-INT4
• https://huggingface.co/baichuan-inc/Baichuan-M3-235B-FP8

✨ Spaces citing this paper:
• https://huggingface.co/spaces/baichuan-inc/Baichuan-M3-Inquiry

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SEMA: Simple yet Effective Learning for Multi-Turn Jailbreak Attacks

📝 Summary:
A novel framework called SEMA is introduced that effectively trains multi-turn attackers for large language models without relying on existing strategies or external data, achieving state-of-the-art a...

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06854
• PDF: https://arxiv.org/pdf/2602.06854

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs

📝 Summary:
Residual binarization framework RaBiT addresses feature co-adaptation in quantized LLMs through hierarchical path derivation and robust initialization, achieving superior accuracy-efficiency trade-off...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05367
• PDF: https://arxiv.org/pdf/2602.05367

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions

📝 Summary:
OdysseyArena presents a new framework for evaluating large language models on long-horizon, inductive agent tasks that emphasize autonomous discovery of environmental transition laws. AI-generated sum...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05843
• PDF: https://arxiv.org/pdf/2602.05843

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SeeUPO: Sequence-Level Agentic-RL with Convergence Guarantees

📝 Summary:
SeeUPO is a critic-free reinforcement learning method that ensures convergence guarantees in multi-turn agent interactions by modeling sequential decision-making as multi-agent bandit problems and usi...

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06554
• PDF: https://arxiv.org/pdf/2602.06554

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.

👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.

👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.

👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.

👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.

👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.

👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.

👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.

👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.

👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.

💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.

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✨Avoiding Premature Collapse: Adaptive Annealing for Entropy-Regularized Structural Inference

📝 Summary:
Optimal transport models face premature mode collapse and instability during annealing as standard cooling is too fast. EPH-ASC, an adaptive algorithm, solves this by enforcing a linear stability law, preventing gradient explosions and stabilizing training.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23039
• PDF: https://arxiv.org/pdf/2601.23039

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare

📝 Summary:
RLVR methods using group sampling suffer from bias toward likely trajectories and missed rare-correct ones; a difficulty-aware advantage scaling technique improves performance on benchmarks without in...

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06717
• PDF: https://arxiv.org/pdf/2602.06717

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Seg-ReSearch: Segmentation with Interleaved Reasoning and External Search

📝 Summary:
Seg-ReSearch introduces a novel segmentation approach that combines interleaved reasoning with external search to overcome limitations of frozen MLLM knowledge, using hierarchical reward design for tr...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04454
• PDF: https://arxiv.org/pdf/2602.04454
• Github: https://github.com/iSEE-Laboratory/Seg-ReSearch

✨ Datasets citing this paper:
• https://huggingface.co/datasets/iSEE-Laboratory/OK_VOS

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Vision Transformer Finetuning Benefits from Non-Smooth Components

📝 Summary:
Vision transformer components exhibit varying plasticity levels that correlate with finetuning performance, challenging the assumption that smoothness is always beneficial. AI-generated summary The sm...

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06883
• PDF: https://arxiv.org/pdf/2602.06883
• Github: https://github.com/ambroiseodt/vit-plasticity

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨AudioSAE: Towards Understanding of Audio-Processing Models with Sparse AutoEncoders

📝 Summary:
AudioSAE applies sparse autoencoders to Whisper and HuBERT models, extracting stable acoustic and semantic features. These features disentangle information, reduce false speech detections, and correlate with human EEG, demonstrating practical utility.

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05027
• PDF: https://arxiv.org/pdf/2602.05027

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#AudioAI #SparseAutoencoders #MachineLearning #SpeechRecognition #Neuroscience