TL;DR
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A preprint reports that a 27-billion-parameter terminal agent improved its scores through five stages combining verified examples and reinforcement learning. Researchers designed and managed those stages, so the results do not establish that an AI can improve itself without human involvement.
A research team reports that a 27-billion-parameter terminal agent raised its score on Terminal-Bench 4.0 from 0.00% to 19.84% after five training stages, but the preprint does not show an AI improving itself without human involvement. The researchers chose and ran the stages, set training parameters and selected checkpoints, making the result evidence for a managed training pipeline rather than autonomous recursive self-improvement.
The 26-page preprint, Terminal-RSI, is attributed to Tencent’s Hy Frontier lab and four universities. It describes training an open model called Qwen3.8-27B on a pool of 3,000 tasks. The final checkpoint, Terminal-RSI-27B, was evaluated on four Terminal-Bench suites. The paper reports an equal-weight average across those suites of 50.58%, compared with 20.68% for the base model. Scores are the mean of three evaluation runs.
The five stages alternated Recursive Self-Rewrite (RSR) and Recursive Policy-Refine (RPR): Base, RSR-1, RPR-1, RSR-2 and RPR-2. In RSR, the model’s attempts were checked by a task verifier; accepted attempts were turned into step-by-step plans, screened and run again. Only plans that passed the verifier again were retained as training examples. RPR used reinforcement learning to increase the likelihood of higher-scoring behavior. People set up and supervised this process.
Results varied by benchmark. Terminal-Bench 3.0 rose from 0.00% to 16.95%, Terminal-Bench 2.1 from 51.70% to 86.52%, and Terminal-Bench Hard from 31.00% to 79.00%. On the long-horizon benchmark cited in the source, the final model completed an average of 5 of 46 tasks, compared with zero for the base model. On SWR100, a set of tasks held out from training, the score was 6.00% versus 3.00% for the base model and remained flat after the third stage.
Terminal-RSI, Checked What the results show
A five-stage training pipeline raised benchmark scores for a terminal agent. Researchers designed and managed the process, so the results do not demonstrate self-improvement without human involvement.
How training worked
Verified successful attempts became training examples; reinforcement learning then encouraged higher-scoring behavior.
The agent tried terminal tasks through a command line.
Protected tests checked the machine’s final state.
Researchers screened verified plans and ran five stages alternating self-rewrite and policy refinement.
Reported benchmark gains
Scores improved across the named suites, though results varied by benchmark.
| Evaluation | Base | Final |
|---|---|---|
| Terminal-Bench 3.0 | 0.00% | 16.95% |
| Terminal-Bench 2.1 | 51.70% | 86.52% |
| Terminal-Bench Hard | 31.00% | 79.00% |
| SWR100, held out from training | 3.00% | 6.00% |
Who directed the process?
The model contributed verified examples, but people made the key training decisions.
- Generated task attempts
- Produced plans from verified successes
- Designed and supervised the stages
- Set training parameters and selected checkpoints
Why Verifying Completion Matters
The study speaks to a practical weakness in command-line agents: they can stop early or report success without completing the requested work. Terminal-RSI’s training method makes verified completion the basis for retaining examples, rather than accepting an agent’s own claim that it is done. That distinction matters for systems asked to repair builds, update data pipelines or change software, where an incomplete result can appear plausible.
The paper also reports that its reward design changed which behaviors scored well. Under an earlier reward, failed attempts received 50.09% of all reward. The revised reward kept every failure below every success, according to the source account. This points to reward design and reliable checking as important parts of agent training. It does not establish that the approach works across general software tasks or without human direction.
The reported gains are meaningful within the named evaluations, but the final model still completed only a small share of the long-horizon tasks. The scores are benchmark results, not evidence that an agent can independently decide how to improve, train itself and verify broad real-world progress. Readers should distinguish that narrower finding from wider online claims about an AI that has already achieved recursive self-improvement.
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How the Five Stages Worked
A terminal agent interacts with a computer through a command line: it reads files, runs commands, inspects output and tries to complete a task. To judge its work, Terminal-RSI uses a verifier that checks the machine’s final state with protected tests. The paper defines success as passing the complete verifier, rather than relying on an agent’s declaration or a subset of checks.
The researchers’ account gives a concrete reason for that standard: in one set of recorded training attempts, 748 failed runs declared the task complete within ten turns. That count is a raw observation from those attempts; the source does not provide a baseline for converting it into a rate or a broader comparison.
The authors also report a control in which reinforcement learning ran directly from the base model. It scored 0.00% on Terminal-Bench 4.0 at every recorded checkpoint. When the self-rewrite stage came first, the subsequent reinforcement-learning stage reached 16.67%. This comparison supports the paper’s case that the order and combination of training methods mattered in its setup; it does not show that the model independently devised the pipeline.
What the Benchmarks Leave Open
The reported results are from a preprint, and the source does not establish whether the work has been peer reviewed. It also does not provide enough detail here to assess performance outside the named benchmarks, how reliably the gains transfer to new task types, or how much training compute and human labor the full process required.
The long-horizon score remains limited at an average of 5 completed tasks out of 46. The source also describes a case in which the final model passed tests without building what the task asked for. That example underscores a gap between passing a verifier and satisfying the full intent of a task. How often such mismatches occur, and how broadly the verifier catches them, is not specified in the supplied material.
Most directly, the preprint does not test a system that independently chooses its training stages, rewards, learning rates and checkpoint selections. Those decisions were made by researchers. The extent to which later versions could automate parts of that work remains unknown.
Further Tests Could Clarify Scope
The preprint’s next important test is independent scrutiny and replication: whether other researchers can reproduce the reported scores and verify the evaluation procedure. Further evaluations on new tasks, especially longer tasks and cases where tests may not capture the full request, could help establish how well the method transfers.
The source material does not name a publication date, a follow-up experiment or a plan to release additional results. Until such evidence is available, the clearest conclusion is limited to the reported benchmark gains from a human-directed, five-stage training pipeline.
Key Questions
Did Terminal-RSI improve itself without human involvement?
No. The reported pipeline used examples generated from the model’s verified successes, but researchers designed and managed the training stages, set the training parameters and selected checkpoints.
How much did the agent improve on Terminal-Bench 4.0?
The paper reports a rise from 0.00% to 19.84% across the five-stage pipeline. Each score is the mean of three evaluation runs.
What does the 50.58% figure represent?
It is the paper’s equal-weight average across four Terminal-Bench suites for the final model, up from 20.68% for the base model. It is not the score on a single benchmark.
Did the final model solve long tasks reliably?
The reported result was an average of 5 completed tasks out of 46 on the long-horizon benchmark, compared with zero for the base model. That result shows improvement in the reported evaluation, while leaving many tasks incomplete.
Source: Thorsten Meyer AI
