Excited to share these preliminary results on our internal autoresearch system @Recursive_SI, where we achieve SOTA on nanochat / nanogpt speedrun / kernel benchmarks using the same underlying system without task-specific adaptations. blog: recursive.com/articles/first…

How far can we compress the discrete tokens in an LLM's context into compact latent vectors? With the right training recipe at large scale, our Latent Context Language Models (LCLMs) compress context up to 16× and land on a new Pareto frontier for long-context inference. 🧵(1/n)

c you there!

New blog! Is frontier asynchronous RL solved? The blog covers Async RL theory and infrastructure, surveying 8 open-weight frontier labs for the algorithmic techniques and systems fixes to handle train-inference mismatch. Also answered: why do current methods still fail at high policy lag? Which methods scale with horizon and compute?

love the design so much

🚀 slime v0.3.0 is out! This release is a major step toward agent-first RL. We turned slime’s existing multi-turn / agentic capabilities into a more coherent foundation: - slime/agent with reusable sandbox-agent components - OpenAI / Anthropic-compatible adapters - black-box coding-agent RL example - variable global batch-size training - fully async training as a first-class path - lower host-memory usage for more flexible rollout-inference setups - PPO refactor with actor-critic colocation - delta weight sync, FlashQLA for Qwen GDN, --save-hf, and more CI coverage slime is moving closer to a practical open-source framework for large-scale agentic RL. Release note: github.com/THUDM/slime/relea…

omg

Today we're announcing our Series C funding: $355M at a $4.65B valuation, led by some great investors @generalcatalyst and @Redpoint. We've had insane growth in the last year, but we're still very early. So proud of the team and what we have built so far!

really enjoy working with everyone here 💚 amazing place

It's really neat to see all the interest in the Composer 2 technical report, from training to kernel design to inference. If you have any questions about why we did things, feel free to ask. I'll run around the office and bug people.

🫡

This paper by Ivan Lee (@ivn1e) & @BergKirkpatrick was great! Best thing I’ve seen at #COLM2025 so far! Readability ≠ Learnability: Rethinking the Role of Simplicity in Training Small Language Models openreview.net/forum?id=AFMG…

📢 (1/16) Introducing PaTH 🛣️ — a RoPE-free contextualized position encoding scheme, built for stronger state tracking, better extrapolation, and hardware-efficient training. PaTH outperforms RoPE across short and long language modeling benchmarks arxiv.org/abs/2505.16381

amazing Jason, amazing Nexad, please check this out!

Coding agents can debug their own outputs, but what if none of the fixes are correct? We overcome sparse rewards by making them continuous📈 Instead of having binary execution rewards, we introduce a learned verifier to measure how close the current solution is to a correct one📏

I teach a class where students code up an ML library from scratch in Python. Wenting showed me that a Claude Agent (with interactive unit test feedback and the spec) could solve it 100%. We thought it would be fun to scale this idea to every Python library in the world.

So... can agents now build a package from scratch? Test them on Commit0! This is an amazing and fun project this summer! Huge thanks to Wenting and to everyone in the lab for their support and guidance! 🚀👏