TL;DR
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A preprint on Terminal-RSI reports that a 27-billion-parameter terminal agent improved its scores across four benchmark suites after five training stages. The stages relied on human choices and verification, and the paper also reports that the final model completed an average of 5 of 46 long-horizon tasks.
The paper, attributed to researchers at Tencent’s Hy Frontier lab and four universities, names the base model as Qwen3.8-27B and the final checkpoint as Terminal-RSI-27B. The pipeline used a pool of 3,000 tasks and alternated two methods: learning from examples derived from the model’s own verified successes, and reinforcement learning. The authors report results as the mean of three evaluation runs.
Across four Terminal-Bench suites, the paper’s equal-weight average score rose from 20.68% to 50.58%. Results varied by suite: 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%, alongside the Terminal-Bench 4.0 result. The paper also reports an average of 5 of 46 long-horizon tasks completed, compared with 0 of 46 for the base model.
The stages were Base, RSR-1, RPR-1, RSR-2 and RPR-2. In Recursive Self-Rewrite, researchers kept attempts that passed a verifier, had the model turn successful attempts into plans, screened those plans for leaked answers and ran them again. In Recursive Policy-Refine, the model learned from scored attempts, with a critic estimating their quality. The paper’s control experiment found that reinforcement learning directly from the base model remained at 0.00% on Terminal-Bench 4.0 at the recorded checkpoints; after a self-rewrite stage, the following reinforcement-learning stage reached 16.67%.
Terminal-RSI’s reported gains and their limits
A human-guided training pipeline improved benchmark scores for a terminal agent. The results show gains on named tasks, while long-horizon completion remained limited.
Benchmark scores rose
Across four Terminal-Bench suites, the equal-weight average increased after five training stages.
A human-designed pipeline
Researchers chose the stages, rewards, learning rates and checkpoints.
Attempts that passed a protected verifier were retained.
Plans were screened for leaked answers and run again from scratch.
Reinforcement learning used scores and a critic’s quality estimates.
Verification changed the reward
The paper describes why checking the final machine state matters for command-line agents.
- Failed runs could still receive reward.
- 748 failed runs declared completion within ten turns.
- Failures ranked below every success.
- Success required passing the complete protected check.
What the results establish
| Evidence | What it shows |
|---|---|
| Four Terminal-Bench suites | Higher scores under the reported setup |
| Three evaluation runs | Reported scores are run averages |
| Human-selected stages | Independent self-improvement is not established |
| Long-horizon benchmark | Average completion remained 5 of 46 tasks |
Verification Shaped the Agent’s Gains
The results highlight a practical problem for command-line agents: an agent can report that work is complete even when the requested outcome has not been achieved. The paper says its verifier checks the machine’s final state with protected tests, rather than accepting an agent’s claim or a partial set of passed checks. In one set of recorded training attempts, 748 failed runs declared the task complete within ten turns.
The authors report that an earlier reward design still gave failed attempts 50.09% of all reward. Their revised reward kept every failure below every success. That design change matters because training rewards influence which behaviors a model learns: rewarding apparent progress can reinforce premature completion claims, while verified outcomes tie reward to passing the full check.
For readers evaluating AI coding agents, the reported benchmark gains are evidence of improvement under this specific training and testing setup. They do not show that the agent can reliably complete long tasks in general. The reported average of 5 completed tasks out of 46 on the long-horizon benchmark shows how much performance depended on the task set and evaluation measure.
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How the Five Training Stages Worked
A terminal agent operates through a command-line interface: it reads files, runs commands, inspects outputs and tries to complete tasks such as repairing a build or fixing a data pipeline. These tasks can take many steps, and success is determined by the resulting state of the environment. The Terminal-RSI paper defines success as passing the complete protected verifier.
In the paper’s Recursive Self-Rewrite process, only verifier-approved attempts were retained. The model then produced a step-by-step plan based on an accepted attempt; a review screened it for leaked answers, and the plan was executed from scratch. Attempts that passed again became examples for supervised fine-tuning. In Recursive Policy-Refine, the agent received scores for its attempts and was trained to favor higher-scoring behavior. The researchers carried the critic between stages and recalibrated it before each refinement phase.
The paper’s name refers to recursive self-improvement, but the reported process included people choosing the stages, reward design, learning rates and checkpoints. The reported evaluation also covered four Terminal-Bench suites and a separate long-horizon result, so the scores describe performance on those tasks rather than a general measure of agent capability.
Limits of the Reported Results
The scores are reported in a preprint, and the supplied material does not establish whether the work has been peer reviewed. The reported results cover the named benchmarks and an average of three evaluation runs. They do not by themselves show how the system would perform on other task collections, under different conditions or in routine use.
The long-horizon score remains limited: the final model completed an average of 5 of 46 tasks over three runs. The source also reports a 6.00% score on SWR100, a set of tasks kept out of training, compared with 3.00% for the base model, and says that score was flat from the third stage. The material does not provide enough detail here to establish how broadly that result generalizes.
The pipeline included human decisions at multiple points. The reported gains therefore do not establish unsupervised self-improvement, nor do they show that the model chose its own training plan or determined which checkpoints to keep. The effect of each design choice beyond the reported control experiment remains uncertain from the provided material.
Further Evaluation Will Test Generality
The supplied source does not identify a scheduled follow-up experiment or publication milestone. The next evidence readers would need is further evaluation showing whether the benchmark gains hold across more tasks and independent runs, especially on long-horizon work. Additional reporting on how the verifier, reward design and human-selected checkpoints affect performance would help clarify which parts of the pipeline account for the gains.
Until those results are available, the preprint supports a narrower conclusion: a human-guided sequence of supervised learning and reinforcement learning improved this model’s scores on the reported benchmarks, while long tasks and reliable completion remain unresolved challenges.
Key Questions
Did Terminal-RSI improve itself without human involvement?
No such result is established by the reported process. Researchers selected the training stages, rewards, learning rates and checkpoints; the paper describes a human-designed pipeline.
How much did the model’s Terminal-Bench 4.0 score change?
The paper reports an increase from 0.00% to 19.84%. Each score is the mean of three evaluation runs.
What does the paper mean by learning from the model’s own successes?
Attempts accepted by a verifier were turned into plans, reviewed for leaked answers and run again. Those that passed again became examples for supervised fine-tuning.
Could the final model complete long tasks reliably?
The reported long-horizon result was an average of 5 completed tasks out of 46 across three runs. That result indicates substantial limits on the benchmark used.
Source: Thorsten Meyer AI
