Rahul Chand

RahulRahul is the name Buddha gave to his son (it also means silver) ChandChand means "moon" in Sanskrit

राहुल चंद

Hi, I am Rahul. I completed my master's at Stanford 🌲. Most recently, I worked at Skild AI on their post-training deployment team. Before that, I was at GEAR Lab at NVIDIA Research, where I trained robot foundation models (GR00T N1.6) and worked on RL. I was also an intern at NVIDIA's post-training LLM team, working on looped transformers, and contributed to Nemo-RL (async RL).

At Stanford 🌲, I was part of Prof. Dorsa Sadigh's ILIAD lab, working on what makes good robotics data good. Before that, I was a Research Fellow at Microsoft Research on model compression. My current interests are robotics (and trying to find meaning in a post-singularity world).

At Stanford 🌲, I was a Teaching Assistant for:

In the futurefuture meme, I look forward to getting replaced by AGI and working as a Wallace Design Protein Farmerwallace design.

Publications

How Many Demonstrations Does a VLA Need? On the Sample Efficiency of VLA Adaptation

Under Review

Data Retrieval with Importance Weights for Few-Shot Imitation Learning

Amber Xie, Rahul Chand, Dorsa Sadigh, Joey Hejna

CoRL 2025 Oral · Conference on Robot Learning

MAEB: Massive Audio Embedding Benchmark

Adnan El Assadi, Isaac Chung, Chenghao Xiao, Roman Solomatin, Animesh Jha, Rahul Chand, Silky Singh, … Niklas Muennighoff, Kenneth Enevoldsen

NeurIPS 2026 · Evaluations and Datasets Track

Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction

Anirudh Buvanesh*, Rahul Chand*, Yashoteja Prabhu, Manish Gupta, Manik Verma (* = Equal Contribution)

ICLR 2024 · International Conference on Learning Representations

CapsFlow: Optical Flow Estimation with Capsule Networks

Rahul Chand, Rajat Arora, Ram Prabhakar Venkatesh Babu

Open Source

Isaac-GR00T 6000+ ⭐ · Contributor

NVIDIA's open platform for humanoid robot learning — training, fine-tuning, and deploying GR00T foundation models.

gpu_poor 1400+ ⭐

Tool to check VRAM and token/s requirements for any LLM on consumer hardware. Supports ggml, HuggingFace, bitsandbytes, QLoRA, and gradient checkpointing. Used 120k+ times by 20k+ users.

Starter tutorial for inference with LLaMA in C.

Nemo-RL 1k+ ⭐ · Contributor

NeMo RL is NVIDIA's open-source post-training library.

Nemo-Gym 70+ ⭐ · Contributor

NeMo Gym is a framework for building reinforcement learning environments to train LLMs.

Stanford Stanford
NVIDIA NVIDIA
Microsoft Research Microsoft Research
IISc IISc
IIRS IIRS
BITS Pilani BITS Pilani