You always think you're safe until your job becomes a benchmark.
MACHINE LEARNING
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Understanding Probabilistic Response Generation in LLMs
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The real breakthrough isn't the technique itself it's understanding why it works.
— God of Prompt (@godofprompt) 18 décembre 2025
LLMs generate responses probabilistically. The first answer is just the highest-probability path through their training data. But "most probable" doesn't mean "most correct."
When you introduce… pic.twitter.com/6aANnq7UHNThe real breakthrough isn't the technique itself it's understanding why it works. LLMs generate responses probabilistically. The first answer is just the highest-probability path through their training data. But "most probable" doesn't mean "most correct." When you introduce
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Improving AI Model Reasoning Through Adversarial Prompting Techniques
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But here's where it gets insane. The technique doesn't just improve accuracy on the current problem. It actually teaches the model better reasoning patterns for future questions.
When you force devil's advocate mode repeatedly, the model starts internalizing that adversarial -
Elastic Reasoning: Cognitive-Inspired Approach for Large Language Models
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Beyond Fast and Slow: Cognitive-Inspired Elastic Reasoning for Large Language Models Paper: https://
arxiv.org/abs/2512.15089 -
Tesla has best world models for training robots
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Yeah, world models are being used to train robots. Awesome, huh? And who has the best world models? Tesla.
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Echo-N1: AI Learns Human Conversation Through Affective Reinforcement Learning
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What if AI could learn the art of conversation like a human? This research challenges a year of focusing RL on logic, showing it’s possible to optimize AI for personality and emotional depth—and the results outperform leading models. Echo-N1: Affective RL Frontier Paper:
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Snorkel Sponsors Community Effort to Reduce Code Quality Issues
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Awesome work @GOrlanski
! At Snorkel, we know quality matters. We're excited to sponsor and support the community working on reducing code slop -
Experimenting with Olmo 3 and DeepSeek V3.2 GRPO tweaks
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Oh yeah you probably will. I am currently running tons of experiments to decide which of the Olmo 3 and DeepSeek V3.2 GRPO tweak I should add in the final version.
No side objective really except I'd do an honest evaluation to make sure that the model actually learns well. -
Model Convergence Shifts AI Differentiation to Data Foundations
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On @TBPN
, Databricks CEO and co-founder @alighodsi joined @johncoogan and @jordihays to discuss how model capabilities are converging, and why differentiation increasingly comes from strong data foundations and reasoning over proprietary data. They also talked about how -
Differentiable Programming: Representation vs Learnability Challenge
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The ability to embed your discrete function on a differentiable curve is a prerequisite for differentiable programming, but it is *far* from sufficient, the hard part is getting GD to find the right function Anything can be represented, not everything can be learned