Excited to share that our paper "1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities" has won the Best Paper Award at NeurIPS '25!
Hope to see you all in San Diego :)
Happy to share that our work “1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities” was accepted to #NeurIPS2025 as an Oral Presentation!
Grateful to my amazing collaborators @IJ_Apps@m_bortkiewicz@ben_eysenbach 🥳
1/ While most RL methods use shallow MLPs (~2–5 layers), we show that scaling up to 1000-layers for contrastive RL (CRL) can significantly boost performance, ranging from doubling performance to 50x on a diverse suite of robotic tasks.
Webpage+Paper+Code: https://t.co/43xwfJEIjh
1/ While most RL methods use shallow MLPs (~2–5 layers), we show that scaling up to 1000-layers for contrastive RL (CRL) can significantly boost performance, ranging from doubling performance to 50x on a diverse suite of robotic tasks.
Webpage+Paper+Code: https://t.co/43xwfJEIjh
We (@RohitDilip8 @AlexBeatson@IJ_Apps + I) won 2nd place & the @trychroma prize at the @scale_AI GenAI hackathon w/ Protex: the protein therapeutics universe is immense, we focus the search space w/ @OpenAI & @Meta ESM embeddings of InterPro, Chroma vector db, InfoNCE & @vercel