John Schulman

Introducing Chroma Context-1, a 20B parameter search agent. > pushes the pareto frontier of agentic search > order of magnitude faster > order of magnitude cheaper > Apache 2.0, open-source

I always dreamed of AGI as a wise advisor for humanity. Although LLMs are great for coding & knowledge work, I wouldn’t trust them to give me advice on my career, business strategy, or policy preferences. How can we build AI systems optimized for wisdom? At Mantic we believe the unlock is prediction: predicting world events as accurately as possible, and hill-climbing this single metric. Today we share some recent progress on the Thinking Machines website, having found Tinker a great platform for our RL experiments. TL;DR: We RL-tune gpt-oss-120b to become a better forecaster than any other model. Having good scaffolding is a prerequisite. A fun result: our tuned model + Grok are decorrelated from the other best models, and so are the most indispensable when picking a team.

hmm I sort of disagree and I am bullish for TML. I think they really really have the top talents that I admire in the field, e.g. Jeremy and Sam for optimization, Songlin for Attn, Lia for MoE, Andrew for FSDPv2, and a bunch more folks it's just natural that it takes a while to publish good models: - dpsk starts to publish papers in 2023, even piblished dspkv2 (which I think is already amazing) in mid 2024 and nobody cares, until dpskv3 and r1 - msh took 10+ month to deliver a first not bad long ctx model in 2023 and be silent for the whole 2024 year, and starts to catch up gradually in 2025 - qwen starts to be a much better model than llama until qwen2.5, mid or late 2024, while the lab has been there forever it takes time to get infra and data done, but as long as you have good folks, and principled ways of doing science and experiments, some time or later, scaling laws will pay back

Since Tinker launched, our community has used it to train state-of-the-art models, build infrastructure, and publish novel research. We will be highlighting this creative work in regular roundups, and hope to inspire your own Tinkering as well.

Weirdly, I actually think Yann is making an important point here that is getting lost in semantics. Human intelligence also has jagged frontiers, we're just used to the shape.