Kimi k1.5: Next-Gen LLM with RL for Multimodal Reasoning | Benchmark Performance

Kimi k1.5: Next-Gen LLM with RL for Multimodal Reasoning | Benchmark Performance

ByAmar Jaiswal

Reinforcement learning (RL) has revolutionized AI at its core by enabling models to learn iteratively through interaction and feedback. When applied to large language models (LLMs), RL unlocks new opportunities for dealing with tasks involving sophisticated reasoning, e.g., math problem-solving, programming, and multimodal data interpretation. Classical approaches are greatly dependent on pretraining with massive static…

Multi-Level Deep Q-Networks: Taking Reinforcement Learning Forward

Multi-Level Deep Q-Networks: Taking Reinforcement Learning Forward

ByAmar Jaiswal

The field of reinforcement learning (RL) has significantly transformed machine learning by allowing agents to acquire optimal behaviors via their interactions with the environment. Deep Q-Networks (DQNs) have advanced RL by integrating deep neural networks for the purpose of approximating Q-values, which fundamentally serve to forecast the long-term value associated with executing a particular action…