to pretrain is to learn to compress the universe. and as byproduct, learning to simulate all natural processes gather all the data you can. compress. backpropagate you don't see elegance in that?
LLMS
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AI Researchers Race to Perfect Reinforcement Learning Scaling
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there’s a palpable tension in the air as hundreds of AI researchers (including me!) quietly work nights and weekends trying to figure out the “right way” to scale RL math & code are not the universe we will not rest until post-training is as clean and elegant as pre-training
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Why Do LLMs Gravitate Towards Laziness in Reasoning
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I find it quite curious that LLMs gravitate towards laziness. As far as I know, reasoning LLMs are not penalised for using more compute, nor is o3 here actually using less compute when it takes five turns to deliberate whether it should do the task properly. Maybe training on
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o3-pro hides executed code, wastes visual tokens on CV2 scripts
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o3-pro doesn’t give the executed code in the summary but yes — I’m sure it’s just writing short CV2 scripts and wasting visual tokens triple-checking crops of the output. I just thought it was funny that this was the challenge.
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Frontier Models Medical Imaging: Hallucinations Limit Second Opinion
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A note on this: We have enough evidence from controlled studies that it is likely smart to ask a frontier model for a second opinion. But it is also worth noting that the weakest link in both studies & reality is AI’s ability to “see” medical images. Hallucinations are common.
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JEPA as LLM Plus: Implementation Challenges Explained
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You could argue that JEPA is just LLM+tinything. Although getting tinything to actually work right is pretty hard.
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Yann’s ASI Theory Beyond LLM Integration Strategy
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Yann doesn't have a prior. He has a theory based on years of study. His theory doesn't need to change based on 01. He hasn't claimed llm will at least partially be part of asi. But since lots of other folks are already working on that bit, his best use of time is on other bits.
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VeRL and LangGraph Integration for Multi-Turn RL Agents
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VeRL + LangGraph A seamless integration combining VeRL's reinforcement learning with LangGraph for enhanced multi-turn RL agents. Streamlines training-to-production workflows with automated tool handling and conversation management. Explore the implementation:
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LangConnect: Complete RAG Management Solution with Streamlit
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LangConnect: RAG Made Easy A complete RAG management solution with Streamlit dashboard for document processing and vector search. Built with PostgreSQL/pgvector, it offers real-time processing, multi-type search, and secure authentication. Check it out here
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DataFrame Analyzer: Local Private Pandas Workflow with ChatOllama
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DataFrame Analyzer Streamline your Pandas workflow with this LangChain solution using ChatOllama for local, private DataFrame analysis. Automatically transform complex datasets into clear, human-readable reports. Check out the implementation https://
towardsdatascience.com/llms-pandas-ho
w-i-use-generative-ai-to-generate-pandas-dataframe-summaries-2/
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