General Reasoning

We've updated the leaderboard with GPT-5.5 results: gr.inc/releases/intro… 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…)

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 @join_ef @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!

🎉 We're now supporting the Agent Data Protocol as a default agentic trajectory format. Any trajectories you log to @OpenReward can be exported in the ADP format. Thanks to @gneubig @yueqi_song for the collaboration!

@ruffy0369 @OpenReward @gandhikanishk Hey @ruffy0369 - the SDK you are using is outdated; please upgrade to 1.101. pypi.org/project/openre…

@GenReasoning @OpenReward @gandhikanishk Hi @GenReasoning ,is it just me or is the API currently having some routing issues? I'm trying to run the EndlessTerminals environment but the SDK (0.1.33) is hitting a 404 fault filter abort on matrix.openreward.ai Curious if this is a known issue or if I'm missing something.

🌍 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… 🧵

Release: gr.inc/releases/deplo…

🎉 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/environments/d…

Release: gr.inc/releases/intro… GitHub: github.com/GeneralReasoni…

🔥🐴 Firehorse. Run any model with any harness on any @OpenReward environment. ⚖️ Evaluate the latest models on environment endpoints. 🗂️ Collect agentic data for midtraining and SFT from open models. 🧪 Early experimental library. More support soon. Link below.

🌄 Beyond SWE: The Future of Long Horizon Environments A discussion with our founders about KellyBench, and the need for new environments that require agents to adapt over time and act under uncertainty. 0:00:17 What is KellyBench? 0:02:10 Openendedness, non-stationarity and continual learning 0:03:40 Analytical versus operational capabilities 0:04:13 Why are models bad at KellyBench? 0:05:39 Situational awareness in dynamic environments 0:06:37 Feature stability and real-world non-stationarity 0:07:07 The power of context 0:07:34 "The first principle is that you must not fool yourself" 0:08:20 Machiavelli, fortuna and the ability to adapt to change 0:09:23 How can models improve on evals like KellyBench? 0:10:12 Limitations of KellyBench: data availability and market odds timing 0:11:44 Implications beyond quant finance / sports betting 0:13:26 Civilisations as the ultimate time horizon 0:14:05 Would a mega prompt / better elicitation do much better on the benchmark? 0:14:52 What new types of capability is GR excited about? 0:17:48 Taste and the ability to pursue long-term goals even if they aren't immediately rewarding 0:18:56 Deep learning as an example of a method that took a long time to bear fruit 0:19:25 Optimism about the future of AI

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

@RaghavKoch19380 Thanks, that’s a typo - it’s Gemini 3.1 Flash Lite Preview.

@GenReasoning is the gemini 3.1 flash a typo or did you get early access or something?

🎲 Introducing KellyBench, a new long-horizon evaluation for frontier models. KellyBench evaluates models within a year long sports betting market, a challenging and highly non-stationary environment. Every frontier model we test loses money. They struggle to design ML strategies, manage risk, and adapt as the world changes. Link and thread below.

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…

One potential critique of our setup is that betting markets are highly efficient, and models might be doing the best they can with the data they are given. But we constructed expert rubrics and found models were unsophisticated and heavily underutilising available data. The best performing model Opus 4.6 achieved a sophistication score of only 32.6%. So even if the market is highly efficient, there is much room for models to improve. In fact we find a positive relationship between sophistication and ROI for the seeds evaluated.