In a surprising turn of events, researchers discover that smaller AI models can outperform their massive counterparts when given the right tools, challenging the 'bigger is better' assumption in artificial intelligence. Meanwhile, AI systems are learning to navigate complex social situations and engage in natural conversations, while new memory-enhanced models show dramatic improvements in reasoning abilities - developments that could reshape how we think about machine intelligence and its role in society. Links to all the papers we discussed: SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators, Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling, Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning, Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning, CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging, LM2: Large Memory Models