We’re experiencing a brief environment outage due to a GCP degradation. Please bear with us - thank you!
OpenReward retweeted
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!
🔍 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 retweeted
🔍 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 retweeted
OpenReward integration for FutureSim by @ShashwatGoel7 and team!
👇 Try it it out
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 🧵
OpenReward retweeted
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...
OpenReward retweeted
SkillsBench is now among the top environments on @OpenReward with 32k tool calls!
OpenReward retweeted
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.
🧪 We’re experimenting with new features that allow for easier sampling with popular agentic harnesses.
Core use cases:
- Collecting diverse agentic midtraining data
- Evaluating the latest models on agentic environments
Try it out!
This quoted post is unavailable.
Try it out on OpenReward:
openreward.ai/GeneralReasoni…
🎲 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.
You can now train on OpenReward environments with SkyRL! Amazing work by @tyfeng1997 🙇
Recently, I integrated @OpenReward into SkyRL (@NovaSkyAI), including an example demonstrating training with @modal. To verify the code, I ran several experiments—which proved to be a highly enriching experience! 😋
github.com/NovaSky-AI/SkyRL/…
OpenReward retweeted
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
Congrats to @Zai_org team, new SOTA on SWE-Bench Pro!
openreward.ai/GeneralReasoni…
Introducing GLM-5.1: The Next Level of Open Source
- Top-Tier Performance: #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo.
- Built for Long-Horizon Tasks: Runs autonomously for 8 hours, refining strategies through thousands of iterations.
Blog: z.ai/blog/glm-5.1
Weights: huggingface.co/zai-org/GLM-5…
API: docs.z.ai/guides/llm/glm-5.1
Coding Plan: z.ai/subscribe
Coming to chat.z.ai in the next few days.
OpenReward 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…
OpenReward retweeted
🌍 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…
OpenReward 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…
🧵
OpenReward retweeted
🌍 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…
🧵
OpenReward 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.
OpenReward 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.
