Performance and Scaling

• The model is a scaled-up version of the earlier 1B release, now optimized heavily through critic-based RL (labeled RL2) and domain-specific policy distillation to handle complex sub-agent tasks.1:51
• Performance is highly task-dependent: it manages logic and hard math tasks efficiently, but failed a request for a 5,000-word essay, producing a short, insufficient output.7:55
• Broad claims that the model "beats 4B models" are misleading; while it edges out competitors on some benchmarks, it falls significantly behind Qwen 3.5 4B on SWE-Bench Pro and Terminal Bench.6:33

Agentic Capabilities

• The model's strongest utility is in tool-delegation, where it demonstrates a reliable ability to defer to external tools rather than hallucinating answers from internal knowledge.12:36
• In advanced testing on a GGUF/Llama CPP stack, the model passed 8 out of 8 complex tool-use scenarios involving multi-tool orchestration, failure retries, and distractor bait.10:40
• Tool-use reliability appears backend-sensitive; the speaker reported that the same model returned incorrect formatting under SGLang, suggesting configuration issues may plague specific deployment setups.10:10