Kimi k1.5 and DeepSeek R1 Enhance Scalable Multimodal Reasoning with High Compute Costs
Researchers have introduced Kimi k1.5, a next-generation multimodal large language model (LLM) that utilizes reinforcement learning (RL) to enhance scalable multimodal reasoning. This development aims to improve benchmark performance in AI applications. In a related advancement, DeepSeek’s R1 model demonstrates the capability to learn reasoning through pure RL, albeit with high computational costs. Both models represent significant strides in the integration of RL with LLMs, focusing on improving reasoning capabilities and performance metrics in artificial intelligence.
Sources
arXivGPT
🏷️:Kimi k1.5: Scaling Reinforcement Learning with LLMs 🔗:https://t.co/PJ7YAgrHmD https://t.co/NQPp72ms4B
- arXivGPT
🏷️:DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning 🔗:https://t.co/lmGzXjkBZr https://t.co/GjxQXRXJbt
AI PapersKimi k1.5: Scaling Reinforcement Learning with LLMs. https://t.co/141UA3oUOA
Additional media
