✨Fast-SAM3D: 3Dfy Anything in Images but Faster
📝 Summary:
Fast-SAM3D addresses slow 3D reconstruction by dynamically adapting computation to varying complexity. It uses heterogeneity-aware mechanisms to achieve up to 2.67x faster inference with negligible quality loss, setting a new efficiency standard.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05293
• PDF: https://arxiv.org/pdf/2602.05293
• Github: https://github.com/wlfeng0509/Fast-SAM3D
==================================
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#3DReconstruction #ComputerVision #DeepLearning #AI #Efficiency
📝 Summary:
Fast-SAM3D addresses slow 3D reconstruction by dynamically adapting computation to varying complexity. It uses heterogeneity-aware mechanisms to achieve up to 2.67x faster inference with negligible quality loss, setting a new efficiency standard.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05293
• PDF: https://arxiv.org/pdf/2602.05293
• Github: https://github.com/wlfeng0509/Fast-SAM3D
==================================
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✨Approximation of Log-Partition Function in Policy Mirror Descent Induces Implicit Regularization for LLM Post-Training
📝 Summary:
Policy mirror descent for LLMs struggles with partition function estimation. PMD-mean approximates this with mean reward, implicitly adding a chi-squared regularizer. This enhances robustness and stability, improving LLM post-training performance.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05933
• PDF: https://arxiv.org/pdf/2602.05933
• Github: https://github.com/horizon-rl/OpenKimi
==================================
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#LLM #PolicyMirrorDescent #ReinforcementLearning #MachineLearning #Regularization
📝 Summary:
Policy mirror descent for LLMs struggles with partition function estimation. PMD-mean approximates this with mean reward, implicitly adding a chi-squared regularizer. This enhances robustness and stability, improving LLM post-training performance.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05933
• PDF: https://arxiv.org/pdf/2602.05933
• Github: https://github.com/horizon-rl/OpenKimi
==================================
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✨A Unified Framework for Rethinking Policy Divergence Measures in GRPO
📝 Summary:
This paper presents a unified framework for policy divergence measures in reinforcement learning. It introduces the KL3 estimator as a key constraint, which improves GRPO training stability and performance by promoting stronger exploration.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05494
• PDF: https://arxiv.org/pdf/2602.05494
==================================
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#ReinforcementLearning #MachineLearning #AI #GRPO #PolicyOptimization
📝 Summary:
This paper presents a unified framework for policy divergence measures in reinforcement learning. It introduces the KL3 estimator as a key constraint, which improves GRPO training stability and performance by promoting stronger exploration.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05494
• PDF: https://arxiv.org/pdf/2602.05494
==================================
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✨Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing
📝 Summary:
Focus-dLLM accelerates long-context dLLM inference with a training-free attention sparsification framework. It predicts unmasked regions using confidence-guided indicators and prunes redundant attention while preserving influential sinks across layers. This achieves over 29 times lossless speedup...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02159
• PDF: https://arxiv.org/pdf/2602.02159
• Github: https://github.com/Longxmas/Focus-dLLM
==================================
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📝 Summary:
Focus-dLLM accelerates long-context dLLM inference with a training-free attention sparsification framework. It predicts unmasked regions using confidence-guided indicators and prunes redundant attention while preserving influential sinks across layers. This achieves over 29 times lossless speedup...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02159
• PDF: https://arxiv.org/pdf/2602.02159
• Github: https://github.com/Longxmas/Focus-dLLM
==================================
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❤1
✨Towards Reducible Uncertainty Modeling for Reliable Large Language Model Agents
📝 Summary:
Large language models require uncertainty quantification frameworks that account for interactive agent behavior rather than traditional single-turn question answering scenarios. AI-generated summary U...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05073
• PDF: https://arxiv.org/pdf/2602.05073
==================================
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📝 Summary:
Large language models require uncertainty quantification frameworks that account for interactive agent behavior rather than traditional single-turn question answering scenarios. AI-generated summary U...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05073
• PDF: https://arxiv.org/pdf/2602.05073
==================================
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❤1
✨Privileged Information Distillation for Language Models
📝 Summary:
This paper introduces pi-Distill and OPSD, methods to distill privileged information PI to language models acting without it. They jointly train a PI-conditioned teacher and unconditioned student. These algorithms effectively transfer PI capabilities, outperforming standard fine-tuning and RL.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04942
• PDF: https://arxiv.org/pdf/2602.04942
==================================
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📝 Summary:
This paper introduces pi-Distill and OPSD, methods to distill privileged information PI to language models acting without it. They jointly train a PI-conditioned teacher and unconditioned student. These algorithms effectively transfer PI capabilities, outperforming standard fine-tuning and RL.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04942
• PDF: https://arxiv.org/pdf/2602.04942
==================================
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✨MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents
📝 Summary:
MemSkill introduces a learnable and evolvable memory system for LLM agents. It dynamically selects and refines memory operations via a controller, executor, and designer. This closed-loop process improves memory management and outperforms fixed systems.
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02474
• PDF: https://arxiv.org/pdf/2602.02474
• Project Page: https://viktoraxelsen.github.io/MemSkill/
• Github: https://github.com/ViktorAxelsen/MemSkill
==================================
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📝 Summary:
MemSkill introduces a learnable and evolvable memory system for LLM agents. It dynamically selects and refines memory operations via a controller, executor, and designer. This closed-loop process improves memory management and outperforms fixed systems.
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02474
• PDF: https://arxiv.org/pdf/2602.02474
• Project Page: https://viktoraxelsen.github.io/MemSkill/
• Github: https://github.com/ViktorAxelsen/MemSkill
==================================
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✨Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory
📝 Summary:
Infinite-World is a robust interactive world model that maintains coherent visual memory over 1000+ frames through hierarchical pose-free memory compression, uncertainty-aware action labeling, and rev...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02393
• PDF: https://arxiv.org/pdf/2602.02393
• Project Page: https://rq-wu.github.io/projects/infinite-world/index.html
🔹 Models citing this paper:
• https://huggingface.co/MeiGen-AI/Infinite-World
==================================
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📝 Summary:
Infinite-World is a robust interactive world model that maintains coherent visual memory over 1000+ frames through hierarchical pose-free memory compression, uncertainty-aware action labeling, and rev...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02393
• PDF: https://arxiv.org/pdf/2602.02393
• Project Page: https://rq-wu.github.io/projects/infinite-world/index.html
🔹 Models citing this paper:
• https://huggingface.co/MeiGen-AI/Infinite-World
==================================
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❤1
✨SocialVeil: Probing Social Intelligence of Language Agents under Communication Barriers
📝 Summary:
SocialVeil is a new environment simulating communication barriers to test LLM social intelligence under realistic conditions. It shows barriers significantly impair LLM performance, reducing mutual understanding by over 45% and increasing confusion by nearly 50%. Adaptation strategies offered onl...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05115
• PDF: https://arxiv.org/pdf/2602.05115
==================================
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📝 Summary:
SocialVeil is a new environment simulating communication barriers to test LLM social intelligence under realistic conditions. It shows barriers significantly impair LLM performance, reducing mutual understanding by over 45% and increasing confusion by nearly 50%. Adaptation strategies offered onl...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05115
• PDF: https://arxiv.org/pdf/2602.05115
==================================
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✨CoPE: Clipped RoPE as A Scalable Free Lunch for Long Context LLMs
📝 Summary:
CoPE introduces a soft clipping method for Rotary Positional Embedding that unifies out-of-distribution mitigation and semantic modeling while enabling effective long-context processing up to 256k len...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05258
• PDF: https://arxiv.org/pdf/2602.05258
• Github: https://github.com/hrlics/CoPE
🔹 Models citing this paper:
• https://huggingface.co/haoranli-ml/Llama-3-8B-CoPE-64k-Base
• https://huggingface.co/haoranli-ml/Llama-3-8B-CoPE-64k-Instruct
• https://huggingface.co/haoranli-ml/Llama-3-8B-RoPE-64k-Base
==================================
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📝 Summary:
CoPE introduces a soft clipping method for Rotary Positional Embedding that unifies out-of-distribution mitigation and semantic modeling while enabling effective long-context processing up to 256k len...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05258
• PDF: https://arxiv.org/pdf/2602.05258
• Github: https://github.com/hrlics/CoPE
🔹 Models citing this paper:
• https://huggingface.co/haoranli-ml/Llama-3-8B-CoPE-64k-Base
• https://huggingface.co/haoranli-ml/Llama-3-8B-CoPE-64k-Instruct
• https://huggingface.co/haoranli-ml/Llama-3-8B-RoPE-64k-Base
==================================
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✨Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning
📝 Summary:
Recent LoRA variants claim improvements, but systematic evaluation shows these gains vanish with proper learning rate tuning. Vanilla LoRA achieves similar peak performance when hyperparameters are optimized, remaining a competitive baseline for LLM fine-tuning.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04998
• PDF: https://arxiv.org/pdf/2602.04998
==================================
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📝 Summary:
Recent LoRA variants claim improvements, but systematic evaluation shows these gains vanish with proper learning rate tuning. Vanilla LoRA achieves similar peak performance when hyperparameters are optimized, remaining a competitive baseline for LLM fine-tuning.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04998
• PDF: https://arxiv.org/pdf/2602.04998
==================================
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✨Failing to Explore: Language Models on Interactive Tasks
📝 Summary:
Language models exhibit limited exploration capabilities in interactive environments, with performance improvements achieved through budget allocation strategies and historical summarization technique...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22345
• PDF: https://arxiv.org/pdf/2601.22345
• Project Page: https://explore-exploit-bench.github.io/
• Github: https://github.com/mahdi-jfri/explore-exploit-bench
==================================
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📝 Summary:
Language models exhibit limited exploration capabilities in interactive environments, with performance improvements achieved through budget allocation strategies and historical summarization technique...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22345
• PDF: https://arxiv.org/pdf/2601.22345
• Project Page: https://explore-exploit-bench.github.io/
• Github: https://github.com/mahdi-jfri/explore-exploit-bench
==================================
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✨DFlash: Block Diffusion for Flash Speculative Decoding
📝 Summary:
DFlash is a speculative decoding framework that uses a lightweight block diffusion model for parallel token drafting. This enables efficient LLM inference by generating draft tokens in a single pass. DFlash achieves over 6x lossless acceleration, significantly outperforming existing methods.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06036
• PDF: https://arxiv.org/pdf/2602.06036
• Project Page: https://z-lab.ai/projects/dflash/
• Github: https://github.com/z-lab/dflash
🔹 Models citing this paper:
• https://huggingface.co/z-lab/Qwen3-Coder-30B-A3B-DFlash
• https://huggingface.co/z-lab/Qwen3-8B-DFlash-b16
• https://huggingface.co/z-lab/Qwen3-4B-DFlash-b16
==================================
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📝 Summary:
DFlash is a speculative decoding framework that uses a lightweight block diffusion model for parallel token drafting. This enables efficient LLM inference by generating draft tokens in a single pass. DFlash achieves over 6x lossless acceleration, significantly outperforming existing methods.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06036
• PDF: https://arxiv.org/pdf/2602.06036
• Project Page: https://z-lab.ai/projects/dflash/
• Github: https://github.com/z-lab/dflash
🔹 Models citing this paper:
• https://huggingface.co/z-lab/Qwen3-Coder-30B-A3B-DFlash
• https://huggingface.co/z-lab/Qwen3-8B-DFlash-b16
• https://huggingface.co/z-lab/Qwen3-4B-DFlash-b16
==================================
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👍1
✨DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers
📝 Summary:
DASH significantly accelerates the Shampoo optimizer by using 3D tensor stacking for better GPU utilization and introducing faster inverse matrix root computations like Newton-DB. This results in up to 4.83x faster optimizer steps and improved model performance.
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02016
• PDF: https://arxiv.org/pdf/2602.02016
• Github: https://github.com/IST-DASLab/DASH
==================================
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📝 Summary:
DASH significantly accelerates the Shampoo optimizer by using 3D tensor stacking for better GPU utilization and introducing faster inverse matrix root computations like Newton-DB. This results in up to 4.83x faster optimizer steps and improved model performance.
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02016
• PDF: https://arxiv.org/pdf/2602.02016
• Github: https://github.com/IST-DASLab/DASH
==================================
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✨Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents
📝 Summary:
Easy Dataset is a framework that synthesizes LLM fine-tuning data from unstructured documents via a GUI and LLMs. It improves domain-specific performance while preserving general knowledge.
🔹 Publication Date: Published on Jul 5, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.04009
• PDF: https://arxiv.org/pdf/2507.04009
• Github: https://github.com/ConardLi/easy-dataset
==================================
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📝 Summary:
Easy Dataset is a framework that synthesizes LLM fine-tuning data from unstructured documents via a GUI and LLMs. It improves domain-specific performance while preserving general knowledge.
🔹 Publication Date: Published on Jul 5, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.04009
• PDF: https://arxiv.org/pdf/2507.04009
• Github: https://github.com/ConardLi/easy-dataset
==================================
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Hierarchical Navigable Small World (HNSW) is an algorithm that makes vector search fast even on huge amounts of data, allowing you to search billions of vectors in milliseconds. The idea of its operation is quite elegant - it's one of the most interesting discoveries of recent years.
Here's how it works:
HNSW builds a multi-level graph, where each upper layer contains exponentially fewer nodes than the layer below.
▪️ All vectors are located in the lower layer (layer 0), which is well connected.
▪️ Only some vectors appear in layer 1, even fewer in layer 2, etc.
▪️ The upper layers work as "fast lanes", allowing you to skip a large number of irrelevant data.
During the search, the algorithm starts from the upper layer, finds the nearest node, descends to the lower layer and repeats the process. By the time you reach the lower layer, you have already narrowed the search to the most relevant environment - there's no need to sort through everything.
This explains why HNSW is so economical with memory. It can "jump over" large amounts of data without evaluating each element.
Key parameters that affect the balance of speed and quality:
▪️ ef - the size of the candidate list during the search
▪️ maxConnections - how many connections each node can have
▪️ distance - a metric for comparing vectors (cosine, dot product, etc.)
Adding new elements works in a similar way: first, we search for the optimal location, then we create connections. Restructuring the graph is resource-intensive, but the queries themselves are performed very quickly.
You can read more in detail here✅
👉 @DataScienceT
Here's how it works:
HNSW builds a multi-level graph, where each upper layer contains exponentially fewer nodes than the layer below.
During the search, the algorithm starts from the upper layer, finds the nearest node, descends to the lower layer and repeats the process. By the time you reach the lower layer, you have already narrowed the search to the most relevant environment - there's no need to sort through everything.
This explains why HNSW is so economical with memory. It can "jump over" large amounts of data without evaluating each element.
Key parameters that affect the balance of speed and quality:
Adding new elements works in a similar way: first, we search for the optimal location, then we create connections. Restructuring the graph is resource-intensive, but the queries themselves are performed very quickly.
You can read more in detail here
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❤1
✨Arch-Router: Aligning LLM Routing with Human Preferences
📝 Summary:
Arch-Router is a 1.5B model that aligns LLM routing with human preferences by matching queries to user-defined domains and action types. It outperforms proprietary models in subjective evaluations and supports flexible addition of new models.
🔹 Publication Date: Published on Jun 19, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.16655
• PDF: https://arxiv.org/pdf/2506.16655
• Project Page: https://huggingface.co/katanemo/Arch-Router-1.5B
• Github: https://github.com/katanemo/archgw/
🔹 Models citing this paper:
• https://huggingface.co/katanemo/Arch-Router-1.5B
• https://huggingface.co/katanemo/Arch-Router-1.5B.gguf
• https://huggingface.co/Mungert/Arch-Router-1.5B-GGUF
✨ Spaces citing this paper:
• https://huggingface.co/spaces/jaimegalanmartinez/f1_faq_engine
• https://huggingface.co/spaces/tejasashinde/archRouter_simulator
• https://huggingface.co/spaces/IsaiahJ04/katanemo-Arch-Router-1.5B
==================================
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📝 Summary:
Arch-Router is a 1.5B model that aligns LLM routing with human preferences by matching queries to user-defined domains and action types. It outperforms proprietary models in subjective evaluations and supports flexible addition of new models.
🔹 Publication Date: Published on Jun 19, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.16655
• PDF: https://arxiv.org/pdf/2506.16655
• Project Page: https://huggingface.co/katanemo/Arch-Router-1.5B
• Github: https://github.com/katanemo/archgw/
🔹 Models citing this paper:
• https://huggingface.co/katanemo/Arch-Router-1.5B
• https://huggingface.co/katanemo/Arch-Router-1.5B.gguf
• https://huggingface.co/Mungert/Arch-Router-1.5B-GGUF
✨ Spaces citing this paper:
• https://huggingface.co/spaces/jaimegalanmartinez/f1_faq_engine
• https://huggingface.co/spaces/tejasashinde/archRouter_simulator
• https://huggingface.co/spaces/IsaiahJ04/katanemo-Arch-Router-1.5B
==================================
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arXiv.org
Arch-Router: Aligning LLM Routing with Human Preferences
With the rapid proliferation of large language models (LLMs) -- each optimized for different strengths, style, or latency/cost profile -- routing has become an essential technique to...
❤3
✨Transformer Explainer: Interactive Learning of Text-Generative Models
📝 Summary:
Transformer Explainer is an interactive web tool enabling non-experts to understand GPT-2's internal workings. It visualizes how the model generates text in real-time based on user input. This improves public access to learning about modern generative AI.
🔹 Publication Date: Published on Aug 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML
==================================
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📝 Summary:
Transformer Explainer is an interactive web tool enabling non-experts to understand GPT-2's internal workings. It visualizes how the model generates text in real-time based on user input. This improves public access to learning about modern generative AI.
🔹 Publication Date: Published on Aug 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML
==================================
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❤1
✨InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning
📝 Summary:
To address high costs and limits in chain-of-thought reasoning, InftyThink uses reinforcement learning to optimize iterative reasoning. It learns to strategically summarize and resume, boosting accuracy by 21% on AIME24, reducing latency, and improving generalization.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06960
• PDF: https://arxiv.org/pdf/2602.06960
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#ReinforcementLearning #AIReasoning #ChainOfThought #ArtificialIntelligence #MachineLearning
📝 Summary:
To address high costs and limits in chain-of-thought reasoning, InftyThink uses reinforcement learning to optimize iterative reasoning. It learns to strategically summarize and resume, boosting accuracy by 21% on AIME24, reducing latency, and improving generalization.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06960
• PDF: https://arxiv.org/pdf/2602.06960
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#ReinforcementLearning #AIReasoning #ChainOfThought #ArtificialIntelligence #MachineLearning