@OpenReward

where machines get reward

openreward.ai
Joined January 2026
  • Tweets33
  • Following3
  • Followers149
2Media
FutureSim is now supported in @OpenReward! Thanks to @GenReasoning for supporting reliable search with date cutoffs. You can test your favorite model + scaffold on it. You can also bring your own data and replay it in our FutureSim environment and see how agents perform!
General Reasoning
@GenReasoningJul 24
🔍 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.
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🔍 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.
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OpenReward integration for FutureSim by @ShashwatGoel7 and team! 👇 Try it it out
Shashwat Goel
@ShashwatGoel7Jun 26
Major FutureSim updates! 🥽 Visualized trajectories: extremely long horizon, 1000s of actions per run and dozens of compactions, with summaries! Goldmine of interesting agent behaviors across Fable 5, GPT 5.5, GLM 5.2 etc! 📈 @OpenReward integration! You can now use FutureSim, incl. on your own data, with our favourite standard! The @GenReasoning was super supportive in making this happen! Some cool observations in 🧵
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TRL v1.4 is out! two things I'm excited about: → chunked NLL loss for SFT. Way less VRAM, same loss, often faster. Qwen3-14B @ 16k seq: 58.9 → 38.9 GB. → first-class @OpenReward integration. One line wires up an env into GRPO. Plus: more chat templates, MFU helpers...
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Xiangyi Li
@xdotliMay 26
SkillsBench is now among the top environments on @OpenReward with 32k tool calls!
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Ai2
@allen_aiApr 30
We built AstaBench to give the field a shared, transparent way to measure whether AI can do rigorous scientific work. We’re pleased to see adoption with the @AISecurityInst via Inspect Evals and @GenReasoning, which added an AstaBench task to OpenReward.
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ƬⲘ@tm23twtApr 8
timelapse 27 :) - submitted the rust reasoning algo env to meta rl hack, (actually built a python then moved to the rust one) created rust dataset around 1000 problems will make it next to 2.5k - define the whole reward logic not the optimal i think designed the way validation works, will refine it & push to @PrimeIntellect & @OpenReward envs. - have some other tasks as well, deadline is Tomorrow so need to finish this - this week was a pretty rough like peak locked in, so will chill & and just relax for few days
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great to see our llm-srbench featured in openreward! super exciting collection of science environments for agents!!
General Reasoning
@GenReasoningApr 7
🌍 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…
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🌍 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…
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Replying to @gandhikanishk
@gandhikanishk's Endless Terminals is the most popular env on OpenReward!
General Reasoning
@GenReasoningMar 31
🌍 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… 🧵
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🌍 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… 🧵
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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!
aashay sachdeva
@AashaySachdevaMar 27
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.
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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
@GenReasoningMar 24
Introducing OpenReward. 🌍 330+ RL environments through one API ⚡ Autoscaled sandbox compute 🍒 4.5M+ unique RL tasks 🚂 Works like magic with Tinker, Miles, Slime Link and thread below.
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Xiangyi Li
@xdotliMar 27
.@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
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Cool to see OpenForecaster environment trending on @OpenReward. Thanks @AashaySachdeva for porting it!
General Reasoning
@GenReasoningMar 24
Introducing OpenReward. 🌍 330+ RL environments through one API ⚡ Autoscaled sandbox compute 🍒 4.5M+ unique RL tasks 🚂 Works like magic with Tinker, Miles, Slime Link and thread below.
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Tinker
@tinkerapiMar 25
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…
General Reasoning
@GenReasoningMar 24
Replying to @GenReasoning
🤝 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….
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Rick Lamers
@ricklamersMar 26
Tinker + OpenReward, if that isn’t democratization of RL post-training I don’t know what is!
Ross Taylor
@rosstaylor90Mar 25
Replying to @rosstaylor90
If you want to experience a warm fuzzy feeling, try out the Tinker example from the cookbook: github.com/OpenRewardAI/open… You just need Tinker and OpenReward API keys.

GitHub - OpenRewardAI/openreward-cookbook: Training and evaluating with OpenReward

Training and evaluating with OpenReward. Contribute to OpenRewardAI/openreward-cookbook development by creating an account on GitHub.

github.com
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Very cool from @rosstaylor90 and the team at General Reasoning - this will be very high impact
General Reasoning
@GenReasoningMar 24
Introducing OpenReward. 🌍 330+ RL environments through one API ⚡ Autoscaled sandbox compute 🍒 4.5M+ unique RL tasks 🚂 Works like magic with Tinker, Miles, Slime Link and thread below.
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RadixArk
@radixarkMar 24
330+ environments, 4.5M+ tasks through one API is impressive. Love seeing native integration with Miles and Slime, makes spinning up RL experiments so much cleaner 🔥
General Reasoning
@GenReasoningMar 24
Introducing OpenReward. 🌍 330+ RL environments through one API ⚡ Autoscaled sandbox compute 🍒 4.5M+ unique RL tasks 🚂 Works like magic with Tinker, Miles, Slime Link and thread below.
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lovish
@louvishhMar 24
this is super cool! i've met @rosstaylor90 and the rest of team, they are doing amazing work 🚀
General Reasoning
@GenReasoningMar 24
Introducing OpenReward. 🌍 330+ RL environments through one API ⚡ Autoscaled sandbox compute 🍒 4.5M+ unique RL tasks 🚂 Works like magic with Tinker, Miles, Slime Link and thread below.
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