Ahead of AI
4 mentions across all digests
Ahead of AI is Sebastian Raschka's newsletter and publication covering LLM research, including curated paper lists, inference-time scaling techniques, and annual reviews of the state of large language models.
Categories of Inference-Time Scaling for Improved LLM Reasoning
Raschka systematizes inference-time compute scaling techniques for LLMs, showing practitioners can achieve 3x reasoning improvement (15%→52% accuracy) by trading inference compute for better outputs without retraining models.
LLM Research Papers: The 2025 List (July to December)
Reasoning models and inference-time scaling dominate H2 2025 LLM research, with RL-augmented training and multimodal systems gaining significant research momentum.
LLM Research Papers: The 2025 List (January to June)
H1 2025 LLM research is dominated by reinforcement learning over pure scale: DeepSeek-R1 and Kimi k1.5 exemplify the shift toward reasoning-optimized models with verifiable rewards.
Understanding and Coding the KV Cache in LLMs from Scratch
KV caches explained: the memory-vs-latency tradeoff that powers efficient LLM inference, from conceptual foundations to working Python code.