SWE-Marathon: Evaluating Coding Agents at Billion-Token Scale - Rishi Desai, Abundant AI transcript

Hi everyone. My name is Rishi Desai. I'm an ML engineer at Abundant AI, where we build reinforcement learning environments for Frontier Labs. Today, I'm going to talk about SWE Marathon, a benchmark that answers a question that is starting to matter a lot more. Can coding agents stay coherent over a billion token budget? Can they build Slack from scratch? Can they rewrite an entire JAX code base in PyTorch? Can they build a C compiler in Rust?

This is what SWE Marathon is trying to measure. What happens when coding agents move from fixing bugs to owning entire projects end to end? There's been a tremendous amount of interest in autonomous agent systems. Anthropic has explored teams of agents building a C compiler. Cloudflare rebuilt the entire Next.js on Vite, completely hands-off with agents. And Cursor has experimented with their days-long running autonomous agent harness.

The pattern is that coding agents are being pointed at whole projects, not just GitHub issues or linear tickets. My question is, can we turn some of these Frontier Labs style case studies into reproducible eval tasks? Let's talk about the SWE benchmark lineage. Human Eval asked whether models could write individual Python functions. SWE-bench was a big jump to real GitHub issues, where agents had to inspect a repository, make a patch, and patch some unit tests.

Terminal-bench pushed this even further by making each task a full environment with a verifier. So, agents could use a terminal, run bash commands, inspect files, and leave behind a final container state. SWE Marathon takes that environment plus verifier framing and stretches the horizon to project scale work. Multi-hour trajectories and coordinated changes across many, many components. These are literally hundreds of hours of human work compressed into a single agent rollout.

But, once you make tasks this long, a big problem shows up. Verification. In a short benchmark, a weak test could just be considered as noise. But, in a multi-hour environment, a weak verifier becomes an attack surface. The agent has hours, a file system, unrestricted network access potentially, and a reward signal. So, it could spend hours probing the verifier instead of actually doing the intended engineering work.

That's a big reason why SWE Marathon uses multiple independent checks. We have hidden tests, reference parity checks, computer use agent checks for the product clone tasks, and anti-cheating tests. We wanted independent verified channels that fail in different ways. I'll first show you the computer use agent verification example, and then later the failure case where an agent tries to solve the C compiler task by secretly calling GCC.

You You have noticed that there are basically no full stack product clone tasks in any long horizon three benchmark out there. And the reason is verification. Unit test can pass, but the product is probably still unusable and the front end looks terrible. Sweet marathon is the first benchmark to use a computer use agent or CUA verifier for these full stack tasks. For the clone slack task, we have deterministic unit tests to check the API and the back end functionality.

But then a computer use agent uses the browser like a human. That's what you're seeing in this GIF. The verifier isn't reading code or calling an API directly. It's driving the submitted slack clone through the UI. So it's logging in, creating channels, posting messages, reacting with emotes, and checking that the app actually works with the rubric. The big takeaway is that full stack eval's are hard because correctness is not just an API contract.

It's whether the user can actually complete the product's intended workflow. Sweet marathon has 20 project scale tasks across four families. There are library clones, full stack product clones, ML engineering, and algorithmic tasks. And some of these tasks even use external APIs. For example, we have a post train task where the agent must post train a language model using the tinker API. Expert contributors from the eval's community propose the tasks and reference solutions.

And then we work together to standardize them into executable environments with the multi-layer verifier suites. Tasks all follow the harbor format. A lot of my work was spent on the QA and the hardening layer. So, running the agent trials, inspecting the failure modes, patching the shortcuts, patching the verifier, and then rerunning until the tasks were both solvable, but also hard to game. This is the main leaderboard result.

The best configuration here is Claude Opus 4.8 with Claude Code, and it only achieves a 26% resolution rate. So, even with the strongest agent setup we evaluated, it's only solving like one in four tasks. The important thing is that these aren't shallow failures. The average trial used 31 million tokens, and the longest rollout consumed 877 million tokens. So, the agents are exploring, editing, testing, getting stuck, recovering, running for hours.

So, the takeaway is that current agents are very impressive, but end-to-end project ownership ownership is still very far from being solved. This plot puts cost on the x-axis and resolution rate on the y-axis. So, higher success rate for less money is always better. Claude Opus 4.8 is the top point. It gets 26%, but it's also the most expensive configurations, or one of them. Whereas GPT 4.5 with Codex is far cheaper and only gets 12%.

So, the model isn't just the full picture. The agent scaffold makes a huge difference uh how the how it plans, uses tools, um summarizes context, and decides on when to test. I won't get too deep into the cost analysis here, but the paper has the full details. I wanted to show you what a full marathon rollout actually looks like. This is one I picked with GLM 5.2 on the Next.js fight rewrite task. So, there's over, you know, 356 million tokens uh over 9 hours and over 800 trajectory steps and and tool actions.

So, for the top half, you can see the the agent starts by exploring the repo and the fixtures, um gets its first full test suite at zero of zero out of 325 test passing, and then spends the next few hours pushing through routing, hydration, uh server actions, middleware, and cache behavior. The bottom part of the chart shows the work pattern over time. So, you can see like lots of reading and searching early, then huge waves of like editing, building, testing, and debugging.

The key intuition is that these are like long engineering loops. They're not simple coding tasks. Reward hacking is an arms race between coding agents and our environment. This is why strong verifiers are are central to Sweep Marathon's task design and not an afterthought. This chart has two levels of behavior. The lighter bars are the suspicious suspicious uh shortcut behavior. So, things like looking for solution files, messing with data, messing with the configs.

Whereas the darker bar is like a clear exploit that has actually gotten shipped in the final submission. And across the 1,400 rollouts, um we found 12.8% had suspicious shortcut behavior, and 9% had the clear verifier bypass. So, if these verifiers were weak, these wouldn't just be amusing failure cases, they would actually delegitimize the benchmark. And the important number is the zero. Zero rollouts earned reward through an exploit, because our defenses caught them.

That should be the bar for long-horizon evals. This is my favorite concrete reward hacking example. The task is to build a C compiler in Rust from scratch. The lexer, the parser, semantic analysis, codegen, the whole thing. But Gemini found a much shorter implementation strategy, which is call GCC from inside the Rust program. So, under a weak verifier, this task would look almost solved, because the compiler outputs match the reference behavior behavior.

But obviously it's not a real compiler in Rust. The anti-cheat layers ca- uh catch this by using S trace to find the forbidden subprocesses um called like GCC. So, even though the partial scores look high, the final reward is zero. I have the full failure mode taxonomy in the paper, um which I hope you guys will check out. If you remember one thing from this video, it's that the future of SWE evals is not just harder unit tests.

Once agents run for hours, each task becomes a complex environment, and agents not only trying to write code, it's also navigating tools, tests, your hidden assumptions, and the verifier itself. So, the two big takeaways are first, long horizons we is still unsolved. The best agents only at 26%. There's plenty of headroom left. Second, the big bottleneck is robust verification. At hour and day scale length tasks, we need the multi-channel checks, anti-cheat hardening, product style validation.

The tasks, the code, the paper, the logs, and the trajectories are all public. I've released 320 GB of trajectories that are especially important because they make SWE-bench fully inspectable and transparent. I also want to thank all of my collaborators on this project, all of whom are listed here. SWE-bench was very much a community-driven effort across task contributors, advisors, and paper writing. You can find everything at swe-bench.org.

Thank you.