Document new Leaderboard entry congrats @ddudek for pointing out ClimbMix, time to GPT-2 is now 2.01 hours, down from 2.76 previously

README.md

@@ -3,7 +3,7 @@
 ![nanochat logo](dev/nanochat.png)
 ![scaling laws](dev/scaling_laws_jan26.png)
 
-nanochat is the simplest experimental harness for training LLMs. It is designed to run on a single GPU node, the code is minimal/hackable, and it covers all major LLM stages including tokenization, pretraining, finetuning, evaluation, inference, and a chat UI. For example, you can train your own GPT-2 capability LLM (which cost ~$43,000 to train in 2019) for only $72 (~3 hours of 8XH100 GPU node) and then talk to it in a familiar ChatGPT-like web UI. On a spot instance, the total cost can be closer to ~$20. More generally, nanochat is configured out of the box to train an entire miniseries of compute-optimal models by setting one single complexity dial: `--depth`, the number of layers in the GPT transformer model (GPT-2 capability happens to be approximately depth 26). All other hyperparameters (the width of the transformer, number of heads, learning rate adjustments, training horizons, weight decays, ...) are calculated automatically in an optimal way.
+nanochat is the simplest experimental harness for training LLMs. It is designed to run on a single GPU node, the code is minimal/hackable, and it covers all major LLM stages including tokenization, pretraining, finetuning, evaluation, inference, and a chat UI. For example, you can train your own GPT-2 capability LLM (which cost ~$43,000 to train in 2019) for only $48 (~2 hours of 8XH100 GPU node) and then talk to it in a familiar ChatGPT-like web UI. On a spot instance, the total cost can be closer to ~$15. More generally, nanochat is configured out of the box to train an entire miniseries of compute-optimal models by setting one single complexity dial: `--depth`, the number of layers in the GPT transformer model (GPT-2 capability happens to be approximately depth 26). All other hyperparameters (the width of the transformer, number of heads, learning rate adjustments, training horizons, weight decays, ...) are calculated automatically in an optimal way.
 
 For questions about the repo, I recommend either using [DeepWiki](https://deepwiki.com/karpathy/nanochat) from Devin/Cognition to ask questions about the repo, or use the [Discussions tab](https://github.com/karpathy/nanochat/discussions), or come by the [#nanochat](https://discord.com/channels/1020383067459821711/1427295580895314031) channel on Discord.
 
@@ -17,8 +17,9 @@ Presently, the main focus of development is on tuning the pretraining stage, whi
 | 1 | 3.04 | 0.74833 | 0.2585 | d24 baseline, slightly overtrained | Jan 29 2026 | 348fbb3 | @karpathy |
 | 2 | 2.91 | 0.74504 | 0.2578 | d26 slightly undertrained **+fp8** | Feb 2 2026 | a67eba3 | @karpathy |
 | 3 | 2.76 | 0.74645 | 0.2602 | bump total batch size to 1M tokens | Feb 5 2026 | 2c062aa | @karpathy |
+| 4 | 2.02 | 0.71854 | 0.2571 | change dataset to NVIDIA ClimbMix | Mar 4 2026 | 324e69c | @ddudek @karpathy |
 
-The primary metric we care about is "time to GPT-2" - the wall clock time needed to outperform the GPT-2 (1.6B) CORE metric on an 8XH100 GPU node. The GPT-2 CORE score is 0.256525. In 2019, the training of GPT-2 cost approximately $43,000 so it is incredible that due to many advances over 7 years across the stack, we can now do so much faster and for well below $100 (e.g. at the current ~$3/GPU/hr, an 8XH100 node is ~$24/hr, so 3 hours is ~$72).
+The primary metric we care about is "time to GPT-2" - the wall clock time needed to outperform the GPT-2 (1.6B) CORE metric on an 8XH100 GPU node. The GPT-2 CORE score is 0.256525. In 2019, the training of GPT-2 cost approximately $43,000 so it is incredible that due to many advances over 7 years across the stack, we can now do so much faster and for well below $100 (e.g. at the current ~$3/GPU/hr, an 8XH100 node is ~$24/hr, so 2 hours is ~$48).
 
 See [dev/LEADERBOARD.md](dev/LEADERBOARD.md) for more docs on how to interpret and contribute to the leaderboard.