✨REAM: Merging Improves Pruning of Experts in LLMs
📝 Summary:
Router-weighted Expert Activation Merging (REAM) is proposed as a novel method for reducing memory requirements in Mixture-of-Experts large language models by grouping and merging expert weights inste...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04356
• PDF: https://arxiv.org/pdf/2604.04356
• Project Page: https://bknyaz.github.io/blog/2026/moe/
• Github: https://github.com/SamsungSAILMontreal/ream
🔹 Models citing this paper:
• https://huggingface.co/bknyaz/Qwen3-Coder-Next-REAM
• https://huggingface.co/SamsungSAILMontreal/Qwen3-30B-A3B-Instruct-2507-REAM
• https://huggingface.co/bknyaz/Qwen3-Next-80B-A3B-Instruct-REAM
==================================
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📝 Summary:
Router-weighted Expert Activation Merging (REAM) is proposed as a novel method for reducing memory requirements in Mixture-of-Experts large language models by grouping and merging expert weights inste...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04356
• PDF: https://arxiv.org/pdf/2604.04356
• Project Page: https://bknyaz.github.io/blog/2026/moe/
• Github: https://github.com/SamsungSAILMontreal/ream
🔹 Models citing this paper:
• https://huggingface.co/bknyaz/Qwen3-Coder-Next-REAM
• https://huggingface.co/SamsungSAILMontreal/Qwen3-30B-A3B-Instruct-2507-REAM
• https://huggingface.co/bknyaz/Qwen3-Next-80B-A3B-Instruct-REAM
==================================
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✨Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
📝 Summary:
Personalized RewardBench evaluates reward models' ability to capture individual user preferences, revealing significant challenges in current models and demonstrating superior correlation with downstr...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07343
• PDF: https://arxiv.org/pdf/2604.07343
• Project Page: https://huggingface.co/datasets/QiyaoMa/Personalized-RewardBench
• Github: https://github.com/Martin-qyma/Personalized-RewardBench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/QiyaoMa/Personalized-RewardBench
==================================
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📝 Summary:
Personalized RewardBench evaluates reward models' ability to capture individual user preferences, revealing significant challenges in current models and demonstrating superior correlation with downstr...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07343
• PDF: https://arxiv.org/pdf/2604.07343
• Project Page: https://huggingface.co/datasets/QiyaoMa/Personalized-RewardBench
• Github: https://github.com/Martin-qyma/Personalized-RewardBench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/QiyaoMa/Personalized-RewardBench
==================================
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✨MARS: Enabling Autoregressive Models Multi-Token Generation
📝 Summary:
MARS is a fine-tuning method that enables autoregressive language models to predict multiple tokens per forward pass without architectural changes, maintaining accuracy while improving throughput and ...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07023
• PDF: https://arxiv.org/pdf/2604.07023
==================================
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📝 Summary:
MARS is a fine-tuning method that enables autoregressive language models to predict multiple tokens per forward pass without architectural changes, maintaining accuracy while improving throughput and ...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07023
• PDF: https://arxiv.org/pdf/2604.07023
==================================
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✨MoRight: Motion Control Done Right
📝 Summary:
MoRight is a unified framework that enables disentangled motion control and causal relationship modeling in video generation, allowing separate manipulation of object motion and camera viewpoint while...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07348
• PDF: https://arxiv.org/pdf/2604.07348
• Project Page: https://research.nvidia.com/labs/sil/projects/moright/
==================================
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📝 Summary:
MoRight is a unified framework that enables disentangled motion control and causal relationship modeling in video generation, allowing separate manipulation of object motion and camera viewpoint while...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07348
• PDF: https://arxiv.org/pdf/2604.07348
• Project Page: https://research.nvidia.com/labs/sil/projects/moright/
==================================
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✨Neural Computers
📝 Summary:
Neural Computers represent a new computing paradigm where models function as runtime systems, learning to execute tasks through I/O traces rather than explicit programming. AI-generated summary We pro...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06425
• PDF: https://arxiv.org/pdf/2604.06425
• Project Page: https://metauto.ai/neuralcomputer/
==================================
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📝 Summary:
Neural Computers represent a new computing paradigm where models function as runtime systems, learning to execute tasks through I/O traces rather than explicit programming. AI-generated summary We pro...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06425
• PDF: https://arxiv.org/pdf/2604.06425
• Project Page: https://metauto.ai/neuralcomputer/
==================================
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✨RAGEN-2: Reasoning Collapse in Agentic RL
📝 Summary:
Research identifies template collapse in multi-turn LLM agents as a hidden failure mode undetectable by entropy, proposing mutual information proxies and SNR-aware filtering to improve reasoning quali...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06268
• PDF: https://arxiv.org/pdf/2604.06268
• Project Page: https://ragen-ai.github.io/v2/
• Github: https://github.com/mll-lab-nu/RAGEN
==================================
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📝 Summary:
Research identifies template collapse in multi-turn LLM agents as a hidden failure mode undetectable by entropy, proposing mutual information proxies and SNR-aware filtering to improve reasoning quali...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06268
• PDF: https://arxiv.org/pdf/2604.06268
• Project Page: https://ragen-ai.github.io/v2/
• Github: https://github.com/mll-lab-nu/RAGEN
==================================
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✨Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment
📝 Summary:
Multilingual retrieval models exhibit bias toward English documents in mixed-language document pools, which is addressed through a novel training strategy that improves cross-lingual alignment with mi...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05684
• PDF: https://arxiv.org/pdf/2604.05684
==================================
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📝 Summary:
Multilingual retrieval models exhibit bias toward English documents in mixed-language document pools, which is addressed through a novel training strategy that improves cross-lingual alignment with mi...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05684
• PDF: https://arxiv.org/pdf/2604.05684
==================================
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✨INSPATIO-WORLD: A Real-Time 4D World Simulator via Spatiotemporal Autoregressive Modeling
📝 Summary:
INSPATIO-WORLD presents a real-time framework for generating high-fidelity dynamic scenes from single videos using spatiotemporal autoregressive architecture and joint distribution matching distillati...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07209
• PDF: https://arxiv.org/pdf/2604.07209
• Project Page: https://inspatio.github.io/inspatio-world/
• Github: https://github.com/inspatio/inspatio-world
==================================
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📝 Summary:
INSPATIO-WORLD presents a real-time framework for generating high-fidelity dynamic scenes from single videos using spatiotemporal autoregressive architecture and joint distribution matching distillati...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07209
• PDF: https://arxiv.org/pdf/2604.07209
• Project Page: https://inspatio.github.io/inspatio-world/
• Github: https://github.com/inspatio/inspatio-world
==================================
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✨VenusBench-Mobile: A Challenging and User-Centric Benchmark for Mobile GUI Agents with Capability Diagnostics
📝 Summary:
VenusBench-Mobile presents a comprehensive evaluation framework for mobile GUI agents that reveals significant performance gaps compared to existing benchmarks, emphasizing the need for more robust re...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06182
• PDF: https://arxiv.org/pdf/2604.06182
• Github: https://github.com/inclusionAI/UI-Venus/tree/VenusBench-Mobile
==================================
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📝 Summary:
VenusBench-Mobile presents a comprehensive evaluation framework for mobile GUI agents that reveals significant performance gaps compared to existing benchmarks, emphasizing the need for more robust re...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06182
• PDF: https://arxiv.org/pdf/2604.06182
• Github: https://github.com/inclusionAI/UI-Venus/tree/VenusBench-Mobile
==================================
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✨FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling
📝 Summary:
A novel two-stage reinforcement learning framework called Sol-RL integrates FP4 quantization with diffusion model alignment to accelerate training while maintaining high-fidelity performance. AI-gener...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06916
• PDF: https://arxiv.org/pdf/2604.06916
• Project Page: https://nvlabs.github.io/Sana/Sol-RL/
==================================
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📝 Summary:
A novel two-stage reinforcement learning framework called Sol-RL integrates FP4 quantization with diffusion model alignment to accelerate training while maintaining high-fidelity performance. AI-gener...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06916
• PDF: https://arxiv.org/pdf/2604.06916
• Project Page: https://nvlabs.github.io/Sana/Sol-RL/
==================================
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✨Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved Reasoning
📝 Summary:
This paper introduces process-driven image generation, an iterative method with interleaved textual and visual reasoning. It decomposes synthesis into planning, drafting, reflecting, and refining steps. Dense step-wise supervision ensures consistency and interpretability of intermediate states.
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04746
• PDF: https://arxiv.org/pdf/2604.04746
==================================
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📝 Summary:
This paper introduces process-driven image generation, an iterative method with interleaved textual and visual reasoning. It decomposes synthesis into planning, drafting, reflecting, and refining steps. Dense step-wise supervision ensures consistency and interpretability of intermediate states.
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04746
• PDF: https://arxiv.org/pdf/2604.04746
==================================
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✨TC-AE: Unlocking Token Capacity for Deep Compression Autoencoders
📝 Summary:
TC-AE is a Vision Transformer-based architecture that improves deep compression autoencoders by addressing token space limitations and enhancing semantic structures through joint self-supervised train...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07340
• PDF: https://arxiv.org/pdf/2604.07340
• Github: https://github.com/inclusionAI/TC-AE
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/TC-AE
==================================
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📝 Summary:
TC-AE is a Vision Transformer-based architecture that improves deep compression autoencoders by addressing token space limitations and enhancing semantic structures through joint self-supervised train...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07340
• PDF: https://arxiv.org/pdf/2604.07340
• Github: https://github.com/inclusionAI/TC-AE
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/TC-AE
==================================
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✨DeonticBench: A Benchmark for Reasoning over Rules
📝 Summary:
DEONTICBENCH presents a benchmark for evaluating large language models on complex, context-specific deontic reasoning tasks drawn from real-world legal and policy domains, supporting both symbolic and...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04443
• PDF: https://arxiv.org/pdf/2604.04443
• Project Page: https://huggingface.co/datasets/gydou/DeonticBench
• Github: https://github.com/guangyaodou/DeonticBench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/gydou/DeonticBench
==================================
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📝 Summary:
DEONTICBENCH presents a benchmark for evaluating large language models on complex, context-specific deontic reasoning tasks drawn from real-world legal and policy domains, supporting both symbolic and...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04443
• PDF: https://arxiv.org/pdf/2604.04443
• Project Page: https://huggingface.co/datasets/gydou/DeonticBench
• Github: https://github.com/guangyaodou/DeonticBench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/gydou/DeonticBench
==================================
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✨The Depth Ceiling: On the Limits of Large Language Models in Discovering Latent Planning
📝 Summary:
Research reveals that large language models can perform latent reasoning with varying depths, but there's a gap between discovering and executing multi-step planning strategies, suggesting limitations...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06427
• PDF: https://arxiv.org/pdf/2604.06427
==================================
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📝 Summary:
Research reveals that large language models can perform latent reasoning with varying depths, but there's a gap between discovering and executing multi-step planning strategies, suggesting limitations...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06427
• PDF: https://arxiv.org/pdf/2604.06427
==================================
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✨Q-Zoom: Query-Aware Adaptive Perception for Efficient Multimodal Large Language Models
📝 Summary:
Q-Zoom enhances MLLM performance by adaptively focusing computational resources on relevant visual regions through dynamic gating and self-distilled region proposal networks, achieving faster inferenc...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.06912
• PDF: https://arxiv.org/pdf/2604.06912
• Project Page: https://yuhengsss.github.io/Q-Zoom/
• Github: https://yuhengsss.github.io/Q-Zoom/
🔹 Models citing this paper:
• https://huggingface.co/YuhengSSS/Q-Zoom-Qwen2.5VL-3B
• https://huggingface.co/YuhengSSS/Q-Zoom-Qwen2.5VL-7B
• https://huggingface.co/YuhengSSS/Q-Zoom-Qwen3VL-4B
==================================
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📝 Summary:
Q-Zoom enhances MLLM performance by adaptively focusing computational resources on relevant visual regions through dynamic gating and self-distilled region proposal networks, achieving faster inferenc...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.06912
• PDF: https://arxiv.org/pdf/2604.06912
• Project Page: https://yuhengsss.github.io/Q-Zoom/
• Github: https://yuhengsss.github.io/Q-Zoom/
🔹 Models citing this paper:
• https://huggingface.co/YuhengSSS/Q-Zoom-Qwen2.5VL-3B
• https://huggingface.co/YuhengSSS/Q-Zoom-Qwen2.5VL-7B
• https://huggingface.co/YuhengSSS/Q-Zoom-Qwen3VL-4B
==================================
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✨Fast Spatial Memory with Elastic Test-Time Training
📝 Summary:
Elastic Test-Time Training with fast spatial memory enables efficient 4D reconstruction through multi-chunk adaptation while maintaining stability against catastrophic forgetting. AI-generated summary...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07350
• PDF: https://arxiv.org/pdf/2604.07350
• Project Page: https://fast-spatial-memory.github.io/
• Github: https://github.com/Mars-tin/fast-spatial-mem
🔹 Models citing this paper:
• https://huggingface.co/marstin/fast-spatial-mem
==================================
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📝 Summary:
Elastic Test-Time Training with fast spatial memory enables efficient 4D reconstruction through multi-chunk adaptation while maintaining stability against catastrophic forgetting. AI-generated summary...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07350
• PDF: https://arxiv.org/pdf/2604.07350
• Project Page: https://fast-spatial-memory.github.io/
• Github: https://github.com/Mars-tin/fast-spatial-mem
🔹 Models citing this paper:
• https://huggingface.co/marstin/fast-spatial-mem
==================================
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✨FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching
📝 Summary:
FlowInOne presents a vision-centric multimodal generation framework that unifies diverse input modalities into a single visual representation, enabling coherent image generation and editing through a ...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06757
• PDF: https://arxiv.org/pdf/2604.06757
• Github: https://csu-jpg.github.io/FlowInOne.github.io/
🔹 Models citing this paper:
• https://huggingface.co/CSU-JPG/FlowInOne
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CSU-JPG/VisPrompt5M
• https://huggingface.co/datasets/CSU-JPG/VPBench
==================================
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📝 Summary:
FlowInOne presents a vision-centric multimodal generation framework that unifies diverse input modalities into a single visual representation, enabling coherent image generation and editing through a ...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06757
• PDF: https://arxiv.org/pdf/2604.06757
• Github: https://csu-jpg.github.io/FlowInOne.github.io/
🔹 Models citing this paper:
• https://huggingface.co/CSU-JPG/FlowInOne
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CSU-JPG/VisPrompt5M
• https://huggingface.co/datasets/CSU-JPG/VPBench
==================================
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✨SEVerA: Verified Synthesis of Self-Evolving Agents
📝 Summary:
Formally Guarded Generative Models enable safe and correct agentic code generation by combining formal specifications with soft objectives, ensuring reliability in autonomous agent systems. AI-generat...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25111
• PDF: https://arxiv.org/pdf/2603.25111
• Github: https://github.com/uiuc-focal-lab/severa
==================================
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📝 Summary:
Formally Guarded Generative Models enable safe and correct agentic code generation by combining formal specifications with soft objectives, ensuring reliability in autonomous agent systems. AI-generat...
🔹 Publication Date: Published on Mar 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25111
• PDF: https://arxiv.org/pdf/2603.25111
• Github: https://github.com/uiuc-focal-lab/severa
==================================
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✨Beyond Hard Negatives: The Importance of Score Distribution in Knowledge Distillation for Dense Retrieval
📝 Summary:
Stratified sampling improves knowledge distillation by preserving the full range of teacher scores, outperforming traditional sampling methods in retrieval tasks. AI-generated summary Transferring kno...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04734
• PDF: https://arxiv.org/pdf/2604.04734
==================================
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📝 Summary:
Stratified sampling improves knowledge distillation by preserving the full range of teacher scores, outperforming traditional sampling methods in retrieval tasks. AI-generated summary Transferring kno...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04734
• PDF: https://arxiv.org/pdf/2604.04734
==================================
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✨Learning to Hint for Reinforcement Learning
📝 Summary:
HiLL is a reinforcement learning framework that adaptively generates hints conditioned on reasoner errors to improve learning signals and transfer performance in group relative policy optimization. AI...
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00698
• PDF: https://arxiv.org/pdf/2604.00698
• Github: https://github.com/Andree-9/HiLL
==================================
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📝 Summary:
HiLL is a reinforcement learning framework that adaptively generates hints conditioned on reasoner errors to improve learning signals and transfer performance in group relative policy optimization. AI...
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00698
• PDF: https://arxiv.org/pdf/2604.00698
• Github: https://github.com/Andree-9/HiLL
==================================
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✨Tunable Soft Equivariance with Guarantees
📝 Summary:
A general framework for constructing soft equivariant models through weight projection into designed subspaces is proposed, demonstrating improved performance and reduced equivariance error across mul...
🔹 Publication Date: Published on Mar 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26657
• PDF: https://arxiv.org/pdf/2603.26657
• Github: https://github.com/ashiq24/soft-equivariance
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✓ https://t.iss.one/DataScienceT
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📝 Summary:
A general framework for constructing soft equivariant models through weight projection into designed subspaces is proposed, demonstrating improved performance and reduced equivariance error across mul...
🔹 Publication Date: Published on Mar 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26657
• PDF: https://arxiv.org/pdf/2603.26657
• Github: https://github.com/ashiq24/soft-equivariance
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research