Its a different way of thinking: This is 10,000 times smaller than modern LLMs, but the result is better that truly thinks before it speaks.
🟢 How TRM works?
1️⃣ Draft answer: the model immediately forms a quick sketch of the solution instead of writing it word by word.
2️⃣ Scratchpad: creates an internal space for logic and intermediate reasoning.
3️⃣ Self-criticism: repeatedly (6 times) checks its reasoning, refining and correcting errors.
4️⃣ Rewriting: based on improved logic, creates a new, more accurate version of the answer.
5️⃣ Iteration: repeats the process up to 16 times until it reaches a confident, logically coherent solution.
🟠 Why TRM is interesting?
☞ Outperformed DeepSeek-R1, Gemini 2.5 Pro and o3-mini in reasoning tasks ARC-AGI 1 and ARC-AGI 2 despite having only 7 million parameters and about 1000 training examples.
☞ Lower computational costs with higher results; high efficiency at low expense.
☞ Proof that inherent logic and architecture can be stronger than just model size. It can be briefly described as: "think before you act."
☞ Powerful reasoning systems become accessible even without huge clusters; the model can run on limited resources.
Github
#TinyRecursiveModels #TRM #DeepLearning #NeuralNetworks
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