We’re building long-run intelligence: AI systems that can work and predict at civilisation-scale time horizons.
London, England
Joined May 2024
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General Reasoning retweeted
General Reasoning CEO @rosstaylor90 explains how their new RLCR experiment taught AI agents to organize themselves, hire teammates, and decide when to DM each other:
"The idea of RLCR is, instead of just trying to optimize single agents, how do you optimize collective structures? Teams, research communities, and eventually economies, markets."
"We optimize an organization in a Slack-like harness. They learn who they should hire, what roles they should hire for, when they should be pinging someone annoyingly in their direct messages versus making a public channel."
"It’s not just another model, it’s a whole new ball game. It’s a whole new direction."
@GenReasoning
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 👇
General Reasoning retweeted
makes experiments way more reproducible, its a nice way to guarantee that solution on the live internet won’t leak your evals, and prevents hacking.
it’s not a big uplift if you already have your own search engine.
General Reasoning folks are even offering it as a service:
🔍 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.
General Reasoning retweeted
great to see our llm-srbench featured in openreward!
super exciting collection of science environments for agents!!
🌍 Environments of the Week
The theme this week...environments for science 👩🔬.
First up, LLM-SR Bench by @ParshinShojaee et al is an environment for evaluating language model agents on scientific equation discovery tasks.
openreward.ai/parshinsh/llms…
General Reasoning retweeted
bring your best harness and see how well it can run a startup :)
shoutout @GenReasoning for the love 🫡
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General Reasoning retweeted
@gandhikanishk's Endless Terminals is the most popular env on OpenReward!
🌍 Environments of the Week
It's been a week since we launched @OpenReward. Here are some of our favourite environments this week - some newly added, some heavily used, and some hidden gems.
First, the most used environment of the week is EndlessTerminals by @gandhikanishk with 830k+ tool calls.
openreward.ai/kanishk/Endles…
🧵
General Reasoning retweeted
Agentic RL environments are becoming critical. We integrated OpenReward (openreward.ai) into Alibaba’s ROLE (alibaba.github.io/ROLL/).
Details: github.com/alibaba/ROLL/pull…
General Reasoning retweeted
We've already seen folks build their own harnesses and find SOTA results with off-the-shelf models.
Every verified solution provides a signal of genuine mathematical discovery!
OpenReward makes it easy to run: openreward.ai/erikw26/Horizo…
Excited to see HorizonMath mentioned in @jackclarkSF's Import AI. I'd be very surprised if any model scores more than 30% by this time next year.
General Reasoning retweeted
Cool idea from @AashaySachdeva: unified environment interfaces like @OpenReward can enable LLM meta-learning research!
Pleased with where things are going with more parts of the stack accessible publically. For e.g. I now look forward to weekly @tinkerapi roundups as much as John Oliver episodes!
Played around with this. This was exactly something I was looking for!
Tried a few things -
Creating an env - pretty dope! end to end claude was able to port it from github with only minor issues. One shotted @ShashwatGoel7 OpenForecaster env here. A lot more people should contribute their own envs. I hope they launch monetisation here.
Running a curator over env tasks during RL - When there are so many tasks, which one should you focus on? This is the auto-curriculum/meta-learning bit. I am still not able to beat random/pass@k but I think signals are there over long run this will help with diversity. This obviously has a power law, every run will have top envs dominating but I feel those 20% random tasks will give a big boost to any model.
optimise the GEPA optimiser - gepa is great but pretty slow. What if we could teach a model to do this better? This was in my list for so long, finally with openreward was able to attempt it.
General Reasoning retweeted
.@benchflow_ai started in 09/24 as unity for benchmarks and a hosting hub with early users from Stanford and Princeton. 4 months before R1 dropped
We stopped after 9 months with 0 traction.
Today our latest work SkillsBench is #1 trending on @OpenReward. Game of eval is just on
General Reasoning retweeted
Played around with this. This was exactly something I was looking for!
Tried a few things -
Creating an env - pretty dope! end to end claude was able to port it from github with only minor issues. One shotted @ShashwatGoel7 OpenForecaster env here. A lot more people should contribute their own envs. I hope they launch monetisation here.
Running a curator over env tasks during RL - When there are so many tasks, which one should you focus on? This is the auto-curriculum/meta-learning bit. I am still not able to beat random/pass@k but I think signals are there over long run this will help with diversity. This obviously has a power law, every run will have top envs dominating but I feel those 20% random tasks will give a big boost to any model.
optimise the GEPA optimiser - gepa is great but pretty slow. What if we could teach a model to do this better? This was in my list for so long, finally with openreward was able to attempt it.
General Reasoning retweeted
OpenReward serves hundreds of RL environments through a single API with autoscaled compute. Plug into Tinker to train agents on millions of tasks from anywhere.
xitter.cf/GenReasoning/status/20…
🤝 OpenReward is interoperable with any training library.
Here we use the SETA environment by @Eigent_AI. We use @tinkerapi for model compute and @OpenReward for environment compute.
This allows you to run agentic RL training from a laptop.
github.com/OpenRewardAI/open….
General Reasoning retweeted
Cool to see OpenForecaster environment trending on @OpenReward.
Thanks @AashaySachdeva for porting it!
