text
💰❌ 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐯𝐞𝐫𝐲 𝐆𝐏𝐔 𝐏𝐨𝐨𝐫 - 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐥𝐚𝐰𝐬 𝐫𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧
💰❌ 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐯𝐞𝐫𝐲 𝐆𝐏𝐔 𝐏𝐨𝐨𝐫 - 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐥𝐚𝐰𝐬 𝐫𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧
new_line
new_line
text
🎆 Good news: 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗼𝗿 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗣𝗮𝗶𝗻𝘁 𝟮𝟬𝟬𝟲!
🎆 Good news: 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗼𝗿 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗣𝗮𝗶𝗻𝘁 𝟮𝟬𝟬𝟲!
new_line
new_line
text
The Chinchilla experiments (by Google DeepMind) ran hundreds of pre-trainings with models >1B parameters (I do not want to imagine how much that cost) to 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗿𝗮𝘁𝗶𝗼 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹 𝘀𝗶𝘇𝗲 𝘃𝘀 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗼𝗸𝗲𝗻𝘀. Why is this question so important?
The Chinchilla experiments (by Google DeepMind) ran hundreds of pre-trainings with models >1B parameters (I do not want to imagine how much that cost) to 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗿𝗮𝘁𝗶𝗼 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹 𝘀𝗶𝘇𝗲 𝘃𝘀 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗼𝗸𝗲𝗻𝘀. Why is this question so important?
new_line
text
Well, you only ever have access to a fixed compute, counted in FLOPs (floating point operations). So if your model is bigger, you will have less compute to train on many tokens, and if you want to train on more tokens, your model will be smaller. When model trainings cost million, you absolutely need to get this right.
Well, you only ever have access to a fixed compute, counted in FLOPs (floating point operations). So if your model is bigger, you will have less compute to train on many tokens, and if you want to train on more tokens, your model will be smaller. When model trainings cost million, you absolutely need to get this right.
new_line
new_line
text
The new paper "Chinchilla Scaling: A replication attempt" by Epoch AI sets on on the ambitious goal of reproducing this.
The new paper "Chinchilla Scaling: A replication attempt" by Epoch AI sets on on the ambitious goal of reproducing this.
new_line
new_line
text
But since the authors do not have infinite money, they decided to directly run their computations from DeepMind's own experiments! They took the figure from the last experiment (cf slide below), measured point positions, picked color codes, and ended up reconstructing the underlying data.
But since the authors do not have infinite money, they decided to directly run their computations from DeepMind's own experiments! They took the figure from the last experiment (cf slide below), measured point positions, picked color codes, and ended up reconstructing the underlying data.
new_line
new_line
text
💥 They then just fit the scaling laws proposed by the Chinchilla Authors, but arrived at wildly different results! They find that as a rough rule of thumb, you should use 20 training tokens for each parameter in your model, instead of the 70 obtained in the original paper. They also point out inconsistencies in the paper, and unrealistically narrow confidence intervals.
💥 They then just fit the scaling laws proposed by the Chinchilla Authors, but arrived at wildly different results! They find that as a rough rule of thumb, you should use 20 training tokens for each parameter in your model, instead of the 70 obtained in the original paper. They also point out inconsistencies in the paper, and unrealistically narrow confidence intervals.
new_line
new_line
text
➡️ This only contradicts the results from the last (out of 3) experiments in the Chinchilla paper. And the model trained at the end of the Chinchilla paper still seems properly scaled.
➡️ This only contradicts the results from the last (out of 3) experiments in the Chinchilla paper. And the model trained at the end of the Chinchilla paper still seems properly scaled.
new_line
new_line
text
✅ But it does show that a tiny bit more theoretical work can go a long way, especially given the huge financial costs that such an error can have!
✅ But it does show that a tiny bit more theoretical work can go a long way, especially given the huge financial costs that such an error can have!
💰❌ 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐯𝐞𝐫𝐲 𝐆𝐏𝐔 𝐏𝐨𝐨𝐫 - 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐥𝐚𝐰𝐬 𝐫𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧

🎆 Good news: 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗼𝗿 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗣𝗮𝗶𝗻𝘁 𝟮𝟬𝟬𝟲!

The Chinchilla experiments (by Google DeepMind) ran hundreds of pre-trainings with models >1B parameters (I do not want to imagine how much that cost) to 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗿𝗮𝘁𝗶𝗼 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹 𝘀𝗶𝘇𝗲 𝘃𝘀 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗼𝗸𝗲𝗻𝘀. Why is this question so important?
Well, you only ever have access to a fixed compute, counted in FLOPs (floating point operations). So if your model is bigger, you will have less compute to train on many tokens, and if you want to train on more tokens, your model will be smaller. When model trainings cost million, you absolutely need to get this right.

The new paper "Chinchilla Scaling: A replication attempt" by Epoch AI sets on on the ambitious goal of reproducing this.

But since the authors do not have infinite money, they decided to directly run their computations from DeepMind's own experiments! They took the figure from the last experiment (cf slide below), measured point positions, picked color codes, and ended up reconstructing the underlying data.

💥 They then just fit the scaling laws proposed by the Chinchilla Authors, but arrived at wildly different results! They find that as a rough rule of thumb, you should use 20 training tokens for each parameter in your model, instead of the 70 obtained in the original paper. They also point out inconsistencies in the paper, and unrealistically narrow confidence intervals.

➡️ This only contradicts the results from the last (out of 3) experiments in the Chinchilla paper. And the model trained at the end of the Chinchilla paper still seems properly scaled.

✅ But it does show that a tiny bit more theoretical work can go a long way, especially given the huge financial costs that such an error can have!
👀
chansung hllj maywell radames victor cstr
🤯
maywell radames louisbrulenaudet KingNish NotSiDDH