Dominick Wirzba
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reacted to sergiopaniego's post with 🚀 1 day ago
New post-training example: SFT on agent traces with TRL
→ A large model works as a coding agent in OpenCode, run through OpenEnv
→ The tasks it solves become the training data
→ A 2B model goes from 5.5% to 34.4% on held-out tasks
About 500 traces get most of the gain. Five times more add only 3 to 4 points, so SFT gets the agent working and RL has to do the rest
Full guide, with the models, traces and every rollout open:
https://github.com/huggingface/trl/tree/main/examples/sft_agent_traces reacted to iliya-radulov's post with 🔥 8 days ago
Field Notes from a Low-Cost Robot Learning Lab: Hardware Churn, Software Whiplash, and What Actually Went Wrong
An informal companion piece documenting the hardware incidents, framework churn, and debugging dead-ends from roughly seven weeks of work. It is written for people building similar setups on a hobbyist budget and is candid about the time lost to causes like loose power cables and swapped ports.
Zenodo: https://zenodo.org/records/23259698
About me: I am an independent researcher. These works are part of a single-operator project on low-cost robot learning. My ORCID is 0000-0001-8943-5083. For questions or collaboration, feel free to reach out via the HF community.
reacted to Saccadic-AI's post with 🔥 9 days ago
One neural network. Five mathematical operations. Different learning times.
We trained a small mixture-of-experts transformer on addition, subtraction, multiplication, division, and exponentiation modulo 113.
The operations didn’t generalize together. Multiplication and division reached 90% held-out accuracy first, followed by exponentiation, subtraction, and finally addition.
Multiplication before addition—the reverse of what we expect from a schoolchild.
The accuracy chart in the linked article shows why a single overall accuracy number can hide an interesting learning story.
Measured with IRIS from Saccadic LLC. More figures and analysis: https://huggingface.co/blog/Saccadic-AI/accuracy-plateaued-the-model-kept-changing
Have you observed a similar ordering in multi-task grokking experiments?Organizations
