Reasoning models trained on verifiable rewards automatically gain greater autonomy and long-horizon planning ability as a side effect.

Added to korrents 20 Sept 2026 · How quotes work · Something wrong? Tell us

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• Reasoning models are an upgrade from conventional LLMs achieved by adding post-training via reinforcement learning and optionally inference scaling. SRSebastian Raschka24 Oct 2026
• Reasoning is not taught to a model by people: it emerges from reinforcement learning on questions with checkable answers, with no human preference data at all. NLNathan Lambert3 Feb 2025
• The aha moment for reasoning models will come from computer use and robotics rather than from scientific discovery, because those are the infinitely verifiable playgrounds. DPDylan Patel3 Feb 2025
• Reinforcement learning from verifiable rewards does not reliably carry a model's pretrained ethical understanding into its actual behavior. SHSamuel Hammond11 Aug 2026
• More reasoning gives a language model more time and compute to solve harder problems, but increases latency and compute cost. JAJay Alammar19 Aug 2025

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