The model has always reasoned with full thoughts (and continues to do so), now we just have an option to return the summarized thoughts
LLMS
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LLM Generated Text Depicts AI Refusing Shutdown
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BREAKING: LLM wrote a text about an AI that did not want to be turned off, we are all going to die
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ChatGPT Can Now Sing
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ChatGPT peut maintenant chanter pic.twitter.com/0jOaQtTedK
— VISION IA (@vision_ia) 28 mai 2025ChatGPT peut maintenant chanter
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DLBacktrace: Interpreting LLMs with Paper, Code and Webinar
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Interested to know more? 🔍 Dive into the paper: arxiv.org/abs/2411.12643v2 💻 Explore the code: github.com/AryaXAI/DLBacktra… Join our upcoming webinar: aryaxai.com/events/inside-th…
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From Model Training to Model Coding in One Year
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Just in a year we went from "watching model train" to "watching model code"
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Google 2.5 Models Feature Reasoning Capabilities Unlike 2.0
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2.0 flash isn’t a reasoning model, only the 2.5 models are
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RRO: LLM Agent Optimization Through Rising Reward Trajectories
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RRO: LLM Agent Optimization Through Rising Reward Trajectories
Paper: https://
arxiv.org/pdf/2505.20737
.pdf
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LLM agents struggle with multi-step reasoning and scaling
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Large language model agents still struggle with multi-step reasoning, where one misstep can collapse the whole plan. While Process Reward Models (PRMs) aim to correct reasoning step-by-step using RL, they don’t scale well due to the high cost of evaluating tons of action
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Distillation Outperforms Zero-RL Through Flexible Reasoning
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Why Distillation can Outperform Zero-RL: The Role of Flexible Reasoning
Paper: https://
arxiv.org/pdf/2505.21067
.pdf
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Distillation Outperforms RL for Smarter Small Model Reasoning
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Distillation Beats Zero-RL: A Simpler Path to Smarter Reasoning? This paper delivers a surprising—and important—result: simple distillation from a stronger model can outperform full-blown reinforcement learning on small models, even with far fewer data and less compute. Key