✨VG-Refiner: Towards Tool-Refined Referring Grounded Reasoning via Agentic Reinforcement Learning

📝 Summary:
VG-Refiner improves visual reasoning by addressing unreliable tool outputs. It uses a two-stage think-rethink mechanism and refinement reward to correct poor tool results. This significantly improves accuracy and correction ability in referring and grounding tasks.

🔹 Publication Date: Published on Dec 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06373
• PDF: https://arxiv.org/pdf/2512.06373
• Github: https://github.com/VoyageWang/VG-Refiner

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#VisualReasoning #ReinforcementLearning #ComputerVision #AIResearch #MachineLearning

✨GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

📝 Summary:
GLM-4.1V-Thinking is a vision-language model using a reasoning-centric training framework. It achieves state-of-the-art multimodal reasoning across various tasks like STEM and long document understanding. The model outperforms larger models and competes with closed-source systems like GPT-4o.

🔹 Publication Date: Published on Jul 1

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/glm-41v-thinking-towards-versatile-multimodal-reasoning-with-scalable-reinforcement-learning
• PDF: https://arxiv.org/pdf/2507.01006
• Github: https://github.com/THUDM/GLM-4.1V-Thinking

🔹 Models citing this paper:
• https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking
• https://huggingface.co/zai-org/GLM-4.5V
• https://huggingface.co/zai-org/GLM-4.6V-Flash

✨ Spaces citing this paper:
• https://huggingface.co/spaces/zai-org/GLM-4.1V-9B-Thinking-Demo
• https://huggingface.co/spaces/zai-org/GLM-4.1V-9B-Thinking-API-Demo
• https://huggingface.co/spaces/akhaliq/anycoder

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#GLM41VThinking #MultimodalAI #VisionLanguageModels #ReinforcementLearning #AIResearch

✨Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning

📝 Summary:
Reinforcement Learning enhances decoding-based regression by introducing sequence-level rewards. This overcomes token-level limitations, improving precision and generalization. It establishes a robust and accurate paradigm for numerical prediction.

🔹 Publication Date: Published on Dec 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06533
• PDF: https://arxiv.org/pdf/2512.06533

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#ReinforcementLearning #MachineLearning #Regression #DataScience #AI

✨MIND-V: Hierarchical Video Generation for Long-Horizon Robotic Manipulation with RL-based Physical Alignment

📝 Summary:
MIND-V generates long-horizon, physically plausible robotic manipulation videos. This hierarchical framework uses semantic reasoning and an RL-based physical alignment strategy to synthesize robust, coherent actions, addressing data scarcity.

🔹 Publication Date: Published on Dec 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06628
• PDF: https://arxiv.org/pdf/2512.06628
• Project Page: https://github.com/Richard-Zhang-AI/MIND-V
• Github: https://github.com/Richard-Zhang-AI/MIND-V

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#Robotics #VideoGeneration #ReinforcementLearning #AI #MachineLearning

✨MOA: Multi-Objective Alignment for Role-Playing Agents

📝 Summary:
MOA is a reinforcement-learning framework for role-playing agents that uses multi-objective optimization and thought-augmented rollout. It simultaneously improves multiple skills like domain knowledge and linguistic style, addressing limitations of prior methods. MOA outperforms strong baselines,...

🔹 Publication Date: Published on Dec 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09756
• PDF: https://arxiv.org/pdf/2512.09756

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#AI #ReinforcementLearning #MultiObjectiveOptimization #RolePlayingAgents #MachineLearning

✨MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining

📝 Summary:
MiMo-7B is a 7B LLM optimized for reasoning through pre-training with data mixing and Multi-Token Prediction. Post-training uses reinforcement learning on math and programming problems. This approach enables MiMo-7B to achieve superior reasoning performance, outperforming larger models and OpenAI...

🔹 Publication Date: Published on May 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.07608
• PDF: https://arxiv.org/pdf/2505.07608
• Github: https://github.com/XiaomiMiMo/MiMo

🔹 Models citing this paper:
• https://huggingface.co/XiaomiMiMo/MiMo-7B-RL
• https://huggingface.co/XiaomiMiMo/MiMo-7B-Base
• https://huggingface.co/XiaomiMiMo/MiMo-7B-RL-0530

✨ Spaces citing this paper:
• https://huggingface.co/spaces/ISEEKYAN/megatron_memory_estimator
• https://huggingface.co/spaces/ISEEKYAN/megatron_memory_estimator_old
• https://huggingface.co/spaces/sizzlebop/ZeroGPU-LLM-Inference

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#LLM #AI #ReinforcementLearning #MachineLearning #Reasoning

✨JustRL: Scaling a 1.5B LLM with a Simple RL Recipe

📝 Summary:
JustRL uses a minimal single-stage RL approach with fixed hyperparameters to achieve state-of-the-art performance on 1.5B reasoning models. It uses less compute and shows stable training, suggesting that complex RL methods for LLMs may be unnecessary and can even hinder exploration.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16649
• PDF: https://arxiv.org/pdf/2512.16649

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#ReinforcementLearning #LLMs #DeepLearning #AIResearch #ModelScaling

✨MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Model for Embodied Task Planning

📝 Summary:
MomaGraph-R1, a vision-language model trained with reinforcement learning, achieves state-of-the-art performance in predicting task-oriented scene graphs and zero-shot task planning in household envir...

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16909
• PDF: https://arxiv.org/pdf/2512.16909
• Github: https://hybridrobotics.github.io/MomaGraph/

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#VisionLanguageModel #EmbodiedAI #ReinforcementLearning #SceneGraphs #Robotics

✨Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience

📝 Summary:
Seed-Prover 1.5 is a formal theorem-proving model that uses agentic reinforcement learning and an efficient scaling workflow. It achieves superior performance in solving undergraduate, graduate, and PhD-level math problems with reduced computational resources. This demonstrates the potential of l...

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17260
• PDF: https://arxiv.org/pdf/2512.17260
• Github: https://github.com/ByteDance-Seed/Seed-Prover

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#TheoremProving #ReinforcementLearning #AI #Mathematics #AI4Math

✨Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs

📝 Summary:
Turn-PPO improves multi-turn reinforcement learning for LLM agents by using a turn-level MDP for advantage estimation. This PPO variant outperforms GRPO and standard PPO, addressing limitations in long-horizon reasoning. It demonstrates effectiveness on WebShop and Sokoban datasets.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17008
• PDF: https://arxiv.org/pdf/2512.17008

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#LLM #ReinforcementLearning #AI #MachineLearning #AgenticAI