A brand new agency-centric method to supervising scales software program AI brokers with 78 examples

Does a curated software floor demonstration construct a software program agent that’s extra highly effective than a large mountain of common instruction knowledge? A crew of researchers from Shanghai Jiaoton College and SII Generative AI Analysis Lab (GAIR) proposes limi (“Much less brokers”)Monitored fine-tuning strategies utilizing the bottom mannequin to remodel into competent software program/analysis brokers 78 pattern. Limi rating 73.5% Common on Agent Bench (FTFC 71.7, RC@3 74.2, SR@3 74.6), sturdy baselines (GLM-4.5 45.1, QWEN3-235B-A22B 24.5, kimi-k2 24.1, deepseek-v3.1 11.9), and even surpassing surpassing haryants 10,000 Pattern –128 occasions much less knowledge.

What precisely is it?

• Company effectivity ideas:limi says that Agent’s skills Extra Scale Information High quality/Construction Greater than the uncooked pattern. The analysis crew fine-tuned the GLM-4.5/GLM-4.5-AIR 78 We report giant advantages on lengthy distances, trajectories (samples) for instruments, and company benches and generalized suites (Tau2-bench, evalplus-he/mbpp, ds-1000, household code).
• Minimal however shut director. Every trajectory (~13k~152k tokens; ~42.4k common) captures the whole multi-turn workflow – mannequin inference, software calls, environmental observations, and extra. SII-CLI Working atmosphere. The duty is “Vibe coding(interactive software program improvement) Analysis Workflow (Search, evaluation, experimental design).

How does it work?

• Fundamental mannequin: GLM-4.5 (355b) and GLM-4.5-Air (106b). Coaching makes use of Slime An SFT framework with equivalent configurations all through the comparability (to separate knowledge results).
• Information building: 60 actual queries from practitioners + synthesis from 18 star Github PRS (tight QA by PhD Annotators). With every question, Limi information the total agent trajectory and completes efficiently internally SII-CLI.
• analysis: Agent Bench (R = 3 rounds) FTFC, SR@3, RC@3; Plus Generalized Suite (Tau2-Airline/Retail Cross^4, Rated HE/MBPP, DS-1000, Science).

outcome

• Company Bench (AVG): 73.5%. Limi vs. GLM-4.5 (+28.4 factors); FTFC 71.7% vs 37.8%;SR@3 74.6% vs 47.4%.
• Information effectivity: Limi (78 Pattern) exceeds the educated GLM-4.5 AFM-CODEAGENTSFT (10,000 samples): 73.5% vs 47.8%–+53.7% Absolute 128× There’s little knowledge. Comparable gaps maintain AFM-Webagent (7,610) and CC-Bench-Traj (260).
• Generalization: Limi common throughout software utilization/coding/scientific computing ~57%exceeds GLM-4.5 and different baselines. With out software entry, Limi nonetheless has a slight lead (50.0% vs 48.7% (for GLM-4.5) reveals inherent advantages past environmental touring.

Key takeout

1. Information effectivity governs scale. The restrict reaches 73.5% Common utilizing AgencyBench Curated trajectoriessurpassing GLM-4.5 (45.1%), displaying a +53.7 factors Advantages over A 10K Pattern SFT Baseline –128 occasions much less samples.
2. The standard of the trajectory is just not bulk. The coaching knowledge is as follows: Lengthy distance, software floor Workflows for joint software program improvement and scientific analysis; SII-CLI The execution stack referenced within the paper.
3. Past metric earnings. Limi stories on the company bench FTFC 71.7%, SR@3 74.6%and powerful RC@3there’s a detailed desk displaying giant margins on the baseline. Generalized Suite (Tau2, evalplus-he/mbpp, ds-1000, smicode) common 57.2%.
4. Works on the entire scale. Superb changes GLM-4.5 (355b) and GLM-4.5-AIR (106b) Each generate giant deltas on the bottom, indicating robustness to mannequin dimension.

The analysis crew will practice a GLM-4.5 variant with 78 curated elder-type software floor trajectories captured in a CLI atmosphere spanning software program engineering and analysis duties. We report a mean of 73.5% on AgencyBench utilizing FTFC, RC@3, and SR@3 metrics. Baseline GLM-4.5 was reported at 45.1%. Comparisons towards AFM codian SFT baselines of 10,000 samples present 73.5% vs. 47.8%. Device-free rankings present endogenous good points (roughly 50.0% for LIMI vs. 48.7% GLM-4.5). The trajectory is multi-turn and token density, highlighting planning, software orchestration, and validation.

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