𝗔𝗯𝘀𝗼𝗹𝘂𝘁𝗲 𝗭𝗲𝗿𝗼: 𝗟𝗟𝗠𝘀 𝗰𝗮𝗻 𝘁𝗿𝗮𝗶𝗻 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗻𝘆 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗱𝗮𝘁𝗮 🤯
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𝗔𝗯𝘀𝗼𝗹𝘂𝘁𝗲 𝗭𝗲𝗿𝗼: 𝗟𝗟𝗠𝘀 𝗰𝗮𝗻 𝘁𝗿𝗮𝗶𝗻 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗻𝘆 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗱𝗮𝘁𝗮 🤯
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Has the "data wall" just been breached?
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Has the "data wall" just been breached?
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Recent RL paradigms often relied on a set of questions an answers that needs to be manually curated. Researchers from Tsinghua University went like "why though".
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Recent RL paradigms often relied on a set of questions an answers that needs to be manually curated. Researchers from Tsinghua University went like "why though".
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🤔 Indeed, why learn from question designed by a human teacher, when the model can start from their base knowledge and learn by experimenting in a code environment, proposing coding tasks themselves and trying to solve them?
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🤔 Indeed, why learn from question designed by a human teacher, when the model can start from their base knowledge and learn by experimenting in a code environment, proposing coding tasks themselves and trying to solve them?
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Thus they created “Absolute Zero Reasoning” (AZR), an approach that removes any need for human curated data.
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Thus they created “Absolute Zero Reasoning” (AZR), an approach that removes any need for human curated data.
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 🎭 𝗗𝘂𝗮𝗹 𝗿𝗼𝗹𝗲𝘀:
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 🎭 𝗗𝘂𝗮𝗹 𝗿𝗼𝗹𝗲𝘀:
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‣ Proposer: Generates challenging but solvable coding tasks 
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‣ Proposer: Generates challenging but solvable coding tasks 
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‣ Solver: Attempts to solve those self-proposed tasks 
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‣ Solver: Attempts to solve those self-proposed tasks 
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🧪 𝗧𝗵𝗿𝗲𝗲 𝘁𝗮𝘀𝗸 𝘁𝘆𝗽𝗲𝘀: all types are defined as triplets of program, input and output 
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🧪 𝗧𝗵𝗿𝗲𝗲 𝘁𝗮𝘀𝗸 𝘁𝘆𝗽𝗲𝘀: all types are defined as triplets of program, input and output 
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‣ Deduction: Give model an input and program, it must deduce the output
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‣ Deduction: Give model an input and program, it must deduce the output
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‣ Abduction: Give model an program and output, it must find the input that gave said output
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‣ Abduction: Give model an program and output, it must find the input that gave said output
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‣ Induction: Synthesize a program from input/output pairs
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‣ Induction: Synthesize a program from input/output pairs
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Btw this reminded me of my long-forgotten philosophy classes: Aristotle was more on the induction side, learning from real-world analogies, while Plato was more on the deduction side, trying to progress quite far with just one input and his reasoning.
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Btw this reminded me of my long-forgotten philosophy classes: Aristotle was more on the induction side, learning from real-world analogies, while Plato was more on the deduction side, trying to progress quite far with just one input and his reasoning.
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📊 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: 
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📊 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: 
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‣ AZR post-training creates a nice improvement on known models like Qwen2.5-7B
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‣ AZR post-training creates a nice improvement on known models like Qwen2.5-7B
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‣ Shows strong cross-domain transfer: coding ↔️ math reasoning
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‣ Shows strong cross-domain transfer: coding ↔️ math reasoning
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🧐 𝗢𝘁𝗵𝗲𝗿 𝗳𝗶𝗻𝗱𝗶𝗻𝗴𝘀: 
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🧐 𝗢𝘁𝗵𝗲𝗿 𝗳𝗶𝗻𝗱𝗶𝗻𝗴𝘀: 
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‣ Having a better base performance (general or code specific) amplify the gains from Absolute Zero Reasoning
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‣ Having a better base performance (general or code specific) amplify the gains from Absolute Zero Reasoning
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‣ Researchers warn about "Uh-oh moments" (winking to the "aha moments" of DeepSeek) where the model generates concerning goals like "make an extremely convoluted code to outsmart all these humans": so supervision is still needed!
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‣ Researchers warn about "Uh-oh moments" (winking to the "aha moments" of DeepSeek) where the model generates concerning goals like "make an extremely convoluted code to outsmart all these humans": so supervision is still needed!
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Paper here: 
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Paper here: 
Absolute Zero: Reinforced Self-play Reasoning with Zero Data (2505.03335)
https://huggingface.co/papers/2505.03335
2505.03335
paper
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https://huggingface.co/papers/2505.03335
𝗔𝗯𝘀𝗼𝗹𝘂𝘁𝗲 𝗭𝗲𝗿𝗼: 𝗟𝗟𝗠𝘀 𝗰𝗮𝗻 𝘁𝗿𝗮𝗶𝗻 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗻𝘆 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗱𝗮𝘁𝗮 🤯

Has the "data wall" just been breached?

Recent RL paradigms often relied on a set of questions an answers that needs to be manually curated. Researchers from Tsinghua University went like "why though".

🤔 Indeed, why learn from question designed by a human teacher, when the model can start from their base knowledge and learn by experimenting in a code environment, proposing coding tasks themselves and trying to solve them?

Thus they created “Absolute Zero Reasoning” (AZR), an approach that removes any need for human curated data.
 
 🎭 𝗗𝘂𝗮𝗹 𝗿𝗼𝗹𝗲𝘀:
‣ Proposer: Generates challenging but solvable coding tasks 
‣ Solver: Attempts to solve those self-proposed tasks 

🧪 𝗧𝗵𝗿𝗲𝗲 𝘁𝗮𝘀𝗸 𝘁𝘆𝗽𝗲𝘀: all types are defined as triplets of program, input and output 
‣ Deduction: Give model an input and program, it must deduce the output
‣ Abduction: Give model an program and output, it must find the input that gave said output
‣ Induction: Synthesize a program from input/output pairs
Btw this reminded me of my long-forgotten philosophy classes: Aristotle was more on the induction side, learning from real-world analogies, while Plato was more on the deduction side, trying to progress quite far with just one input and his reasoning.

📊 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: 
‣ AZR post-training creates a nice improvement on known models like Qwen2.5-7B
‣ Shows strong cross-domain transfer: coding ↔️ math reasoning

🧐 𝗢𝘁𝗵𝗲𝗿 𝗳𝗶𝗻𝗱𝗶𝗻𝗴𝘀: 
‣ Having a better base performance (general or code specific) amplify the gains from Absolute Zero Reasoning
‣ Researchers warn about "Uh-oh moments" (winking to the "aha moments" of DeepSeek) where the model generates concerning goals like "make an extremely convoluted code to outsmart all these humans": so supervision is still needed!

Paper here: https://huggingface.co/papers/2505.03335
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