General Reasoning

Introducing Reinforcement Learning from Collective Rewards (RLCR).

RLCR is a recipe that trains language models as agent organisations, which share a message board and workspace. We use it to train Avalon-M1-Mini (120B) and study its behaviour.

We find a number of interesting results, including:

🛠️ Learnt organisational hierarchies and divisions of labour
🕵️ Increased collective verification, through hires of verifiers and testers
👥 The rise of collective thinking, where agents think more about other agents and how to help them

We believe RLCR can provide a laboratory for studying collective intelligence and multi-agent safety.

Blog post and technical report 👇

🐝 How to Train a Swarm

In our first blog post, we explain how latency pressure can incentivise parallel work, the communication primitives agents need to co-ordinate, and outstanding challenges in training large agent collectives.

gr.inc/blog/how-to-tr…

Hear the story of how we developed Avalon at the London AO Summit hosted by @ARIA_research.

We’ll be talking not only about the results, but the research process of how we got there, and what our future roadmap looks like.

🔍 Introducing BackSearch.

LLMs are increasingly asked to predict the future, but a good backtest requires a snapshot of the internet at a point in time.

BackSearch allows LLMs to search the web as it was on a particular date. It’s great for:

🔮 Forecasting and prediction markets.
📈 Quantitative finance.
🌍 RL environments that simulate the world.
🥶 Freezing websearch for benchmark reproducibility

We’re releasing a narrow slice of our index to begin with, focused on the news domain for 2026. Based on feedback we’ll open up more of our index in subsequent releases.

👇 Try BackSearch out with the link below.

We evaluated recent open models on KellyBench.

Here is what we found:

🏆 GLM 5.2 is new open source SoTA, but still loses -30% on average over 5 runs.
📈 We estimate GLM 5.2 is 6+ months behind the frontier based on KellyBench and internal quant evaluations. (Note: we have not evaluated Fable)
🌗 Kimi K2.6 slightly improves on Kimi K2.5 but still struggles at -60% average RoI.
🐈 Recent Mistral models struggle, obtaining mean RoIs of -78% and -99% respectively.

Leaderboard link and more graphs below.