Transformer Language Models without Positional Encodings Still Learn Positional Information Haviv et al.: https://
arxiv.org/abs/2203.16634 #ArtificialIntelligence #DeepLearning #MachineLearning
@montreal_ai
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Transformers Learn Positional Information Without Explicit Encodings
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Scaling Laws of Synthetic Data for Language Models
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Scaling Laws of Synthetic Data for Language Models Qin et al.: https://
arxiv.org/abs/2503.19551
v2
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APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation
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APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay Prabhakar et al.: https://
arxiv.org/abs/2504.03601 #ArtificialIntelligence #DeepLearning #MachineLearning -
Decentralized Collective World Model for Emergent Agent Communication
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Decentralized Collective World Model for Emergent Communication and Coordination Nomura et al.: https://
arxiv.org/abs/2504.03353 #ArtificialIntelligence #DeepLearning #MachineLearning -
Open-Reasoner-Zero: Scaling Reinforcement Learning on Base Models
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Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model Hu et al.: https://
arxiv.org/abs/2503.24290 #ArtificialIntelligence #DeepLearning #MachineLearning -
NoProp: Training Neural Networks Without Back-propagation
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NoProp: Training Neural Networks without Back-propagation or Forward-propagation Qinyu Li, Yee Whye Teh, Razvan Pascanu: https://
arxiv.org/abs/2503.24322 #ArtificialIntelligence #DeepLearning #MachineLearning -
Foundation Agents: Brain-Inspired, Collaborative, Safe Systems
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Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems Liu et al.: https://
arxiv.org/abs/2504.01990
v1
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Can LLMs Generate Novel Research Ideas? Study with NLP Researchers
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Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers Chenglei Si, Diyi Yang, Tatsunori Hashimoto : https://
arxiv.org/abs/2409.04109 #ArtificialIntelligence #DeepLearning #MachineLearning -
Quality-Diversity Algorithms Discovered Via Meta-Black-Box Optimization
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Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization Faldor et al.: https://
arxiv.org/abs/2502.02190 #ArtificialIntelligence #DeepLearning #MachineLearning -
AlphaZero Bridges Human-AI Knowledge Gap Through Concept Discovery
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Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero Schut et al.: https://
arxiv.org/abs/2310.16410 #ArtificialIntelligence #DeepLearning #MachineLearning