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🧠 Stanford paper might be the key to OpenAI o1’s performance: What’s so effective about Chain of Thought? ⇒ it unlocks radically different sequential tasks!
🧠 Stanford paper might be the key to OpenAI o1’s performance: What’s so effective about Chain of Thought? ⇒ it unlocks radically different sequential tasks!
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💭 Reminder: A Chain of Thought (CoT) means that you instruct the model to “think step by step”. Often it’s literally just putting in the prompt “let’s think step by step.”
💭 Reminder: A Chain of Thought (CoT) means that you instruct the model to “think step by step”. Often it’s literally just putting in the prompt “let’s think step by step.”
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🤔 This method has been shown to be unreasonably effective to increase perf on benchmarks. However why it works so well remains unclear.
🤔 This method has been shown to be unreasonably effective to increase perf on benchmarks. However why it works so well remains unclear.
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Here's the scoop: Transformers are amazing at parallel processing, but they've always struggled with tasks that require sequential reasoning. 
Here's the scoop: Transformers are amazing at parallel processing, but they've always struggled with tasks that require sequential reasoning. 
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⛔️ For instance if you ask them the result of 3^2^2^2^…, with 20 iterations, they’ll nearly always fail.
⛔️ For instance if you ask them the result of 3^2^2^2^…, with 20 iterations, they’ll nearly always fail.
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💡 Indeed, researchers prove mathematically, by assimilating transformers networks to logical circuits, that effectively they cannot solve sequential tasks that require more than a certain threshold of sequences.
💡 Indeed, researchers prove mathematically, by assimilating transformers networks to logical circuits, that effectively they cannot solve sequential tasks that require more than a certain threshold of sequences.
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But CoT enables sequential reasoning:
But CoT enables sequential reasoning:
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- 🧱 Each step in the CoT corresponds to simulating one operation in a complex circuit.
- 🧱 Each step in the CoT corresponds to simulating one operation in a complex circuit.
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- 🔄 This allows the transformer to "reset" the depth of intermediate outputs, overcoming previous limitations.
- 🔄 This allows the transformer to "reset" the depth of intermediate outputs, overcoming previous limitations.
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- 🚀 Thus, with CoT, constant-depth transformers can now solve ANY problem computable by polynomial-size circuits! (That's a huge class of problems in computer science.)
- 🚀 Thus, with CoT, constant-depth transformers can now solve ANY problem computable by polynomial-size circuits! (That's a huge class of problems in computer science.)
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- 🔑 Transformers can now handle tricky tasks like iterated squares (computing 3^2^2^2^2) composed permutations and evaluating circuits - stuff that requires serial computation.
- 🔑 Transformers can now handle tricky tasks like iterated squares (computing 3^2^2^2^2) composed permutations and evaluating circuits - stuff that requires serial computation.
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- 📊 The improvement is especially dramatic for transformers with a limited depth. Empirical tests on four arithmetic problems showed massive accuracy gains with CoT on inherently serial tasks.
- 📊 The improvement is especially dramatic for transformers with a limited depth. Empirical tests on four arithmetic problems showed massive accuracy gains with CoT on inherently serial tasks.
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Main takeaway: Chain-of-thought isn't just a neat trick - it fundamentally expands what transformer models can do!
Main takeaway: Chain-of-thought isn't just a neat trick - it fundamentally expands what transformer models can do!
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Read the paper 👉 
Read the paper 👉 
resource
https://huggingface.co/papers/2402.12875
paper
2402.12875
https://huggingface.co/papers/2402.12875
Chain of Thought Empowers Transformers to Solve Inherently Serial
  Problems (2402.12875)
🧠 Stanford paper might be the key to OpenAI o1’s performance: What’s so effective about Chain of Thought? ⇒ it unlocks radically different sequential tasks!

💭 Reminder: A Chain of Thought (CoT) means that you instruct the model to “think step by step”. Often it’s literally just putting in the prompt “let’s think step by step.”

🤔 This method has been shown to be unreasonably effective to increase perf on benchmarks. However why it works so well remains unclear.

Here's the scoop: Transformers are amazing at parallel processing, but they've always struggled with tasks that require sequential reasoning. 

⛔️ For instance if you ask them the result of 3^2^2^2^…, with 20 iterations, they’ll nearly always fail.

💡 Indeed, researchers prove mathematically, by assimilating transformers networks to logical circuits, that effectively they cannot solve sequential tasks that require more than a certain threshold of sequences.

But CoT enables sequential reasoning:

- 🧱 Each step in the CoT corresponds to simulating one operation in a complex circuit.
- 🔄 This allows the transformer to "reset" the depth of intermediate outputs, overcoming previous limitations.
- 🚀 Thus, with CoT, constant-depth transformers can now solve ANY problem computable by polynomial-size circuits! (That's a huge class of problems in computer science.)
- 🔑 Transformers can now handle tricky tasks like iterated squares (computing 3^2^2^2^2) composed permutations and evaluating circuits - stuff that requires serial computation.
- 📊 The improvement is especially dramatic for transformers with a limited depth. Empirical tests on four arithmetic problems showed massive accuracy gains with CoT on inherently serial tasks.

Main takeaway: Chain-of-thought isn't just a neat trick - it fundamentally expands what transformer models can do!

Read the paper 👉 https://huggingface.co/papers/2402.12875
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