Python-Code-Large is a large-scale corpus of Python source code comprising more than 2 million rows of Python code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the Python ecosystem.
By providing a high-volume, language-specific corpus, Python-Code-Large enables systematic experimentation in Python-focused model training, domain adaptation, and downstream code understanding tasks.
Python-Code-Large addresses the need for a dedicated Python-only dataset at substantial scale, enabling focused research across data science, backend systems, automation, scientific computing, and AI-driven Python environments.
In the Text-to-Video arena, Seedance 2.0 has first secured a spot in the LMArena Top 10, while Kling 3.0 has topped the Artificial Analysis leaderboard, with the Kling family claiming 7 spots in the top 15.
🚀 TRL v0.29.0 introduces trl-training: an agent-native training skill.
This makes the TRL CLI a structured, agent-readable capability, allowing AI agents to reliably execute training workflows such as: - Supervised Fine-Tuning (SFT) - Direct Preference Optimization (DPO) - Group Relative Policy Optimization (GRPO)
We’re excited to see what the community builds on top of this.
If you’re working on AI agents, alignment research, or scalable RL training infrastructure: give TRL v0.29.0 a try! 🤗
I wish we had more ppl that cared about their end products quality instead of the outcomes of it. Its clear that you just said "write a cool blog about anthropic being bad" and didnt even try to make it sound human... Its just sad.