Terrence C. Kim

Paper  Another interesting research paper. RL for Small LLM Reasoning: What Works and What Doesn't. The paper investigates how reinforcement learning (RL) can improve reasoning capabilities in small language models (LLMs) under strict computational constraints. The researchers experimented with a 1.5-billion-parameter model (DeepSeek-R1-Distill-Qwen-1.5B) on 4 NVIDIA A40 GPUs over 24 hours, adapting the Group Relative Policy Optimization (GRPO) algorithm and creating a curated dataset of mathematical reasoning problems. The performance gains were accomplished using only 7,000 samples at a rough cost of $42. Through three experiments, they discovered that small LLMs can achieve rapid reasoning improvements within 50-100 training steps using limited high-quality data, but performance degrades with prolonged training under strict length constraints. Mixing easy and hard problems improved training stability, while cosine rewards effectively regulated output length. Their Open-RS varia...

 This is a quick summary of an interesting paper I read today. Supervising  strong learners by amplifying weak experts .  Iterated Distillation and Amplification (IDA) is a proposed scheme for training machine learning systems that can be robustly aligned with complex human values. The approach draws inspiration from AlphaGoZero's training methodology and is notably similar to expert iteration. The core concept involves two key processes: amplification and distillation. In the amplification phase, a learned model serves as a subroutine in a more powerful decision-making process, similar to how AlphaGoZero uses Monte Carlo Tree Search (MCTS) to improve upon its policy network's choices. The distillation phase then involves training the model to directly predict the results of this amplified process, effectively compressing the improved capabilities into a faster system. IDA aims to address AI safety problems by creating a powerful AI that never intentionally optimizes for ...

The research landscape in early 2025: Research Assistant Development Vision Model Improvements Coding Agent  Post-Training applied Reinforcement Learning Enhanced Embedding Model Tokenization Methods Refinement Data Structures Optimization One of my goal is to create an efficient workflow using research and coding agents to handle routine tasks, while I focus on providing direction in critical areas. At the same time, I am also trying to become a lazy YouTuber with my language model based agents handling the majority of the routine workload. My first task was to find agood toool to generate videos off of the research papers in a creative and entertaining manner.  I've evaluated several voice/text-to-video generation tools, but found most lack intuitive interfaces or sufficient versatility. The market shows numerous platforms with similar capabilities but limited innovation. While many of these tools effecively serve their original purpose, they generally haven't expanded into ...

Introduction Despite being labeled the laggard in the language model race behind OpenAI and Anthropic, Google holds two decisive advantages in 2025's evolving AI landscape: unparalleled high-quality data reserves and compute infrastructure that dwarfs even Meta's formidable 600,000 H100 GPUs. As pre-training scaling laws plateau, these assets become critical differentiators. This is especially important in 2025 when everyone is looking for the killer application that can legitimize the research on language models. Combined with DeepMind's elite research talent and visionary leadership, Google possesses a power that competitors ignore at their peril. Gemma is a family of open-weight large language models (LLMs) developed by Google DeepMind and other teams at Google, leveraging the research and technology behind the Gemini models. Released starting in February 2024, Gemma aims to provide state-of-the-art performance in lightweight formats, making advanced AI accessible for re...