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Terminus-4B Rivals Frontier LLMs in Agent Tasks
๐ก4B SLM beats frontier LLMs on agentic terminal tasks, cuts 30% tokens!
โก 30-Second TL;DR
What Changed
Finetuned Qwen3-4B with SFT and rubric-based RL using LLM-as-judge.
Why It Matters
Terminus-4B lowers costs for agentic systems by offloading terminal tasks to cheap SLMs. It promotes scalable multi-agent architectures without relying on expensive frontier models. Developers can build more efficient coding agents.
What To Do Next
Download Terminus-4B from arXiv:2605.03195 and test as subagent on SWE-Bench.
Who should care:Developers & AI Engineers
Key Points
- โขFinetuned Qwen3-4B with SFT and rubric-based RL using LLM-as-judge.
- โขReduces main agent token usage by ~30% on SWE-Bench Pro and internal C# benchmark.
- โขCloses performance gap and often beats Claude Sonnet/Opus/GPT-5.3-Codex.
- โขImproves main agent reliance on subagent outputs, fewer self-terminal executions.
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Original source: ArXiv AI โ