AI Signals From Tomorrow
Signals from Tomorrow is a podcast channel designed for curious minds eager to explore the frontiers of artificial intelligence. The format is a conversation between Voyager and Zaura discussing a specific scientific paper or a set of them, sometime in a short format and sometime as a deep dive.
Each episode delivers clear, thought-provoking insights into how AI is shaping our world—without the jargon. From everyday impacts to philosophical dilemmas and future possibilities, AI Signals from Tomorrow bridges the gap between cutting-edge research and real-world understanding.
Whether you're a tech enthusiast, a concerned citizen, or simply fascinated by the future, this podcast offers accessible deep dives into topics like machine learning, ethics, automation, creativity, and the evolving role of humans in an AI-driven age.
Join Voyager and Zaura as they decode the AI signals pointing toward tomorrow—and what they mean for us today.
AI Signals From Tomorrow
A Survey of Large Language Model Post-Training Methods
This survey paper (https://arxiv.org/pdf/2502.21321) examines various methods for enhancing the capabilities of large language models (LLMs) after their initial training, emphasizing techniques that improve reasoning, factual accuracy, and alignment with desired behaviors. It explores key strategies like fine-tuning, reinforcement learning (RL), and test-time scaling, which involves optimizing how the model generates responses during use. The paper also discusses different approaches to reward modeling, crucial for RL-based alignment, and presents various search and decoding methods used at inference time to improve reasoning quality. Finally, it highlights current challenges and future research directions in the field of LLM post-training.