4 papers
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
Quantile Reward Policy Optimization: Alignment with Pointwise Regression and Exact Partition Functions
Simon Matrenok, Skander Moalla, Caglar Gulcehre
Aligning large language models with pointwise absolute rewards has so far required online, on-policy algorithms such as PPO and GRPO. In contrast, simpler methods that can leverage…
RAT: Bridging RNN Efficiency and Attention Accuracy via Chunk-based Sequence Modeling
Xiuying Wei, Anunay Yadav, Razvan Pascanu +1
Transformers have become the cornerstone of modern large-scale language models, but their reliance on softmax attention poses a computational bottleneck at both training and infere…
Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards
Daniil Karzanov, Rubén Garzón, Mikhail Terekhov +3
This paper introduces a novel agent-based approach for enhancing existing portfolio strategies using Proximal Policy Optimization (PPO). Rather than focusing solely on traditional…