Before writing "The Master Algorithm", I asked a bunch of top AI researchers if they bought the concept. The two strongest believers were Geoff Hinton and Rich Sutton. The Turing committee seems to agree.
MACHINE LEARNING
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Geoff Hinton’s Path: Failures as Innovation in AI Research
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Geoff Hinton got there first. (He described his own career as a series of failures – chew on that.)
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LoRA Matches Full Fine-Tuning Performance With Proper Implementation
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LoRA Without Regret – Recent Blog from Thinking Machines TL/DR: LoRA actually matches full supervised fine-tuning(SFT) when you get the details right. Nearly same sample efficiency, loss(or better), same final performance. Some plain points:
– Apply LoRA to ALL layers, -
Learning LLM Attention Mechanisms: From Zero to Expert
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– you are
– a random CS grad with 0 clue how LLMs work
– get tired of people gatekeeping with big words and tiny GPUs
– decide to go full monk mode
– 2 years later i can explain attention mechanisms at parties and ruin them – here’s the forbidden knowledge map
– top to bottom, -
Anthropic’s granular role definition technique
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Third technique: role definition goes way deeper than "you are an expert." Anthropic specifies expertise granularly. Weak role definition: You are a software engineer. Anthropic's method: Senior backend engineer with expertise in:
– Distributed systems architecture
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Meta’s Continuous Chain-of-Thought Reasoning for LLMs
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Can reasoning LLMs think better if their Chain-of-Thought is continuous instead of discrete? This Meta paper introduces the first scalable way to train continuous CoTs with reinforcement learning—no need to distill from discrete references. By using "soft" tokens
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Tesla Optimus Learning Kung Fu Demonstrates Advanced Robotics
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Tesla Optimus learning Kung Fu pic.twitter.com/ziEuiiKWn7
— Elon Musk (@elonmusk) 4 octobre 2025Tesla Optimus learning Kung Fu
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100+ Free Step-by-Step AI Agents and RAG Systems Tutorials
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100+ free step-by-step tutorials with code covering: AI Agents RAG Systems Voice AI Agents MCP AI Agents Multi-agent Teams Autonomous Game Playing Agents P.S: Don't forget to subscribe for FREE to access future tutorials.
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DeepSearch: Training Small Reasoning Models More Effectively
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How do you train small reasoning models more effectively? Many AI developers run into the same problem: RL fine-tuning plateaus quickly, especially for 1–2B parameter models. A new approach called DeepSearch offers a neat solution. Instead of only using Monte Carlo Tree
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Generalized Parallel Scaling with Interdependent Generations
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Generalized Parallel Scaling with Interdependent Generations