We also incorporate live decision making on social games (where GPT 5.5 has actually regressed from earlier models, which shouldn't be a huge surprise if you ever tried talking it out of some of its nits).
We are still looking for a way to integrate "logical" intelligence with social intelligence in a less arbitrary way, so I'd take a look at the use case that applies to you (probably coding): https://gertlabs.com/rankings?mode=agentic_coding
2. game rl is fundamentally less useful than coding or work rl
1. These numbers are based on percentiles, which inherently can't be saturated. Most benchmarks operate on something like 0-100% of correct answers, so it's natural to make that assumption when you see our numbers. Perhaps we should divide by 100. We create a modified score based on percentiles against other agents, which rebalances every time we add new entries. So when a new frontier model comes out, all of the existing entries get downweighted if the new model outperforms them. And MiMo V2.5 Pro is a much stronger model than people realize.
2. Agents write code to play most of these games (accounting for ~80% of the combined bench score). There is increasing evidence that nearly identical patterns of weights emerge in different models, trained on different mediums and using different algorithms. Pattern matching and extrapolation don't care if the scenario is a 3D "game" environment or a Salesforce "work RL" environment. Examples of drawing distant connections in different domains can reward similar circuitry.
I do have two questions / critiques:
- The verifier doesn't seem to check for code quality / maintainability, which I would posit is one of the major qualms with SOTA coding models i.e. they lack code 'taste'. Ofc this is a difficult problem to solve at scale, but wanted to point that out nonetheless
- This almost feels written like a critique on SWE Bench Pro. Hopefully they fix the issues with that benchmark!
https://deepswe.datacurve.ai/data/trials/quill-shared-toolba...
It seems like GPT here is failing due to an environment issue of connecting to chromium, even though its local unit tests passed. All the models failed 4/4 and checking Opus it ran into the same problem
I checked some other tasks and they seemed legit, although in general the prompts seem somewhat contrived vs. what a typical user would ask their coding agent (such is the difficulty of benchmark construction)
What they did well: normalizing the harness to mini-swe-agent -- models should be able to generalize to different tools at this point. When they struggle to do that (like most Google models), they're unlikely to be useful in practice. And that kind of generalization is an inherent part of intelligence.
For a benchmark that scales, you need to remove the ceiling and provide environments with measurable goals that are NOT a single correct answer, and sufficiently complex evaluation criteria to scale well beyond the current frontier.
We do this by running multi-agent simulations with large action spaces at https://gertlabs.com/rankings.
We're still relatively unknown in the benchmarking space, but by rotating the pool of environments and ensuring the optimal strategies in the environments themselves are affected by other agents participating in the space, we expect we'll be able to resist contamination as major labs start investing more effort to climb the leaderboard. We've already seen Chinese labs taking an interest.