Details on how to try BackSearch out here: gr.inc/releases/introducing-…

🔍 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.

OpenReward integration for FutureSim by @ShashwatGoel7 and team! 👇 Try it it out

Because of backtest variance, we also record a process-based rubric measure called "sophistication", which we track over time. This uses a human expert rubric to judge the sophistication of the strategies employed. GLM-5.2 shows impressive sophistication compared to other open models, although it still does not surpass the closed SoTA on this metric for any time this year. You can see the full leaderboard and more analysis here: gr.inc/releases/introducing-…

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.

We've updated the leaderboard with GPT-5.5 results: gr.inc/releases/introducing-… TLDR: 30% strategy sophistication (new SoTA), much more efficient than GPT-5.4, but still in the red and systematically underperforming human approaches.

We’re also thankful that KellyBench was featured on the front page of the FT last month! (The British AI neuron has a superposition with football…)

KellyBench is also now available as an arXiv preprint. We'll continue to evaluate new models this year as they come in. arxiv.org/abs/2604.27865

Link here: gr.inc/releases/introducing-… We observe a positive trend in the quality of model strategies, but still weak saturation overall and systematic underperformance compared to human approaches. (Note: we impose no time limits for models to develop strategies; they can use as much inference compute as they see fit)

New models on KellyBench, the benchmark for long-horizon sequential decision making. 🎆 Claude Opus 4.7 - new state-of-the-art, with the highest strategy sophistication recorded on the benchmark (28.5%). But still loses -3.7% on average over five seeds. 🐋 DeepSeek V4 Pro - shows strong feature development capabilities, but scores poorly on overfitting rubrics which harms it in deployment (-47%). Link and more graphs below.

Thanks to @ibragim_bad, Jeff Smith and Giovanni R for judging. Thanks to event partners @joinef @airstreet. You can view the environments contributed at the event below: openreward.ai/environments

🌍 Last month we hosted the Complex Worlds Hackathon in London. Participants built an impressive range of environments, spanning synthetic game pipelines, arable farm management, dynamic vehicle routing, hospital triage, robotics, cybersecurity, and more. Congrats to our winners, Julie Huang and Khalid A!

🎉 Native Harbor support on OpenReward! 🐋 Connect your GitHub repository. We'll build the Docker images for each harbor task and deploy the environment as an API endpoint. 🚂 Train on the deployed tasks with any RL framework. ⚖️ Evaluate on the deployed tasks with any harness. Drop the anchor here and get started below: docs.openreward.ai/environme…

Release: gr.inc/releases/introducing-… GitHub: github.com/GeneralReasoning/…

🌍🇬🇧 Complex Worlds Hackathon, London We're hosting an RL environments hackathon in London on the 25th April, partnering with @joinef and @airstreet Come join us to build the next generation of RL environments that model complex worlds over long horizons! luma.com/ykuhqraj

What is the implication of our results? We think better performance is possible on KellyBench with more inference compute. For example evolutionary "autoresearch" approaches to feature development, or multi-agent scaffolds that simulate different roles in a quantitative fund. But there is a broader point to be made. We believe the field is focusing too much on procedural tasks and not enough on truly long-horizon tasks. In the real world, agents will need to pursue open-ended goals, act under uncertainty, and adapt their approaches as the world changes. KellyBench is a test for these capabilities, and models struggle to perform effectively. KellyBench is the first release of our complex worlds research programme. We will have more to share on this direction soon. Try out KellyBench on OpenReward: openreward.ai/GeneralReasoni…