Filled in empty categories

PyTorchConference2025_GithubRepos.json

@@ -1282,6 +1282,7 @@
   {
     "repo_name": "kraken",
     "repo_link": "https://github.com/meta-pytorch/kraken",
+    "category": "kernel examples",
     "github_about_section": "Triton-based Symmetric Memory operators and examples",
     "contributors_all": 11,
     "contributors_2026_q1": 1,
@@ -1292,6 +1293,7 @@
   {
     "repo_name": "nvshmem",
     "repo_link": "https://github.com/NVIDIA/nvshmem",
+    "category": "distributed computing",
     "github_about_section": "NVIDIA NVSHMEM is a parallel programming interface for NVIDIA GPUs based on OpenSHMEM. NVSHMEM can significantly reduce multi-process communication and coordination overheads by allowing programmers to perform one-sided communication from within CUDA kernels and on CUDA streams.",
     "homepage_link": "https://docs.nvidia.com/nvshmem/api/index.html",
     "contributors_all": 20,
@@ -1300,20 +1302,10 @@
     "contributors_2024": 0,
     "contributors_2023": 0
   },
-  {
-    "repo_name": "OLMo",
-    "repo_link": "https://github.com/allenai/OLMo",
-    "github_about_section": "Modeling, training, eval, and inference code for OLMo",
-    "homepage_link": "https://allenai.org/olmo",
-    "contributors_all": 69,
-    "contributors_2026_q1": 0,
-    "contributors_2025": 16,
-    "contributors_2024": 45,
-    "contributors_2023": 28
-  },
   {
     "repo_name": "kernelbot",
     "repo_link": "https://github.com/gpu-mode/kernelbot",
+    "category": "kernel examples",
     "github_about_section": "Write a fast kernel and see how you compare against the best humans and AI on gpumode.com",
     "homepage_link": "https://www.gpumode.com",
     "contributors_all": 25,
@@ -1325,6 +1317,7 @@
   {
     "repo_name": "openzl",
     "repo_link": "https://github.com/facebook/openzl",
+    "category": "data compression",
     "github_about_section": "A novel data compression framework",
     "homepage_link": "https://openzl.org",
     "contributors_all": 39,
@@ -1336,6 +1329,7 @@
   {
     "repo_name": "torchforge",
     "repo_link": "https://github.com/meta-pytorch/torchforge",
+    "category": "reinforcement learning",
     "github_about_section": "PyTorch-native post-training at scale",
     "homepage_link": "https://meta-pytorch.org/torchforge",
     "contributors_all": 43,
@@ -1347,6 +1341,7 @@
   {
     "repo_name": "open-instruct",
     "repo_link": "https://github.com/allenai/open-instruct",
+    "category": "reinforcement learning",
     "github_about_section": "AllenAI's post-training codebase",
     "homepage_link": "https://allenai.github.io/open-instruct/",
     "contributors_all": 57,
@@ -1358,6 +1353,7 @@
   {
     "repo_name": "prime-rl",
     "repo_link": "https://github.com/PrimeIntellect-ai/prime-rl",
+    "category": "reinforcement learning",
     "github_about_section": "Agentic RL Training at Scale",
     "contributors_all": 58,
     "contributors_2026_q1": 29,
@@ -1368,6 +1364,7 @@
   {
     "repo_name": "SkyRL",
     "repo_link": "https://github.com/NovaSky-AI/SkyRL",
+    "category": "reinforcement learning",
     "github_about_section": "SkyRL: A Modular Full-stack RL Library for LLMs",
     "homepage_link": "https://docs.skyrl.ai/docs",
     "contributors_all": 77,
@@ -1379,6 +1376,7 @@
   {
     "repo_name": "OpenRLHF",
     "repo_link": "https://github.com/OpenRLHF/OpenRLHF",
+    "category": "reinforcement learning",
     "github_about_section": "An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)",
     "homepage_link": "https://openrlhf.readthedocs.io",
     "contributors_all": 93,
@@ -1390,6 +1388,7 @@
   {
     "repo_name": "PipelineRL",
     "repo_link": "https://github.com/ServiceNow/PipelineRL",
+    "category": "reinforcement learning",
     "github_about_section": "A scalable asynchronous reinforcement learning implementation with in-flight weight updates.",
     "homepage_link": "https://arxiv.org/abs/2509.19128",
     "contributors_all": 14,
@@ -1401,6 +1400,7 @@
   {
     "repo_name": "cosmos-predict2.5",
     "repo_link": "https://github.com/nvidia-cosmos/cosmos-predict2.5",
+    "category": "world model",
     "github_about_section": "Cosmos-Predict2.5, the latest version of the Cosmos World Foundation Models (WFMs) family, specialized for simulating and predicting the future state of the world in the form of video.",
     "homepage_link": "https://research.nvidia.com/labs/cosmos-lab/cosmos-predict2.5",
     "contributors_all": 13,
@@ -1412,6 +1412,7 @@
   {
     "repo_name": "AReal",
     "repo_link": "https://github.com/inclusionAI/AReaL",
+    "category": "reinforcement learning",
     "github_about_section": "The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.",
     "homepage_link": "https://www.inclusion-ai.org/AReaL",
     "contributors_all": 89,
@@ -1423,6 +1424,7 @@
   {
     "repo_name": "RLinf",
     "repo_link": "https://github.com/RLinf/RLinf",
+    "category": "reinforcement learning",
     "github_about_section": "RLinf: Reinforcement Learning Infrastructure for Embodied and Agentic AI",
     "homepage_link": "https://rlinf.readthedocs.io",
     "contributors_all": 76,
@@ -1434,6 +1436,7 @@
   {
     "repo_name": "ROLL",
     "repo_link": "https://github.com/alibaba/ROLL",
+    "category": "reinforcement learning",
     "github_about_section": "An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models",
     "homepage_link": "https://alibaba.github.io/ROLL/",
     "contributors_all": 78,
@@ -1441,5 +1444,12 @@
     "contributors_2025": 60,
     "contributors_2024": 0,
     "contributors_2023": 0
+  },
+  {
+    "repo_name": "OLMo-core",
+    "repo_link": "https://github.com/allenai/OLMo-core",
+    "category": "training framework",
+    "github_about_section": "PyTorch building blocks for the OLMo ecosystem",
+    "homepage_link": "https://olmo-core.readthedocs.io/en/latest/"
   }
 ]