Qwen3.8-27B-pi: Effort-Ordered Reasoning for Agentic Coding

Qwen3.8-27B-pi is a fine-tuned version of Qwen3.8-27B designed for Pi, an open-source terminal coding-agent harness that performs repository inspection, file editing, shell commands, and iterative verification. The project addresses a flaw in the base model’s effort controls, where low reasoning settings could consume more tokens and sometimes solve fewer tasks than medium settings, by combining supervised fine-tuning on successful full agent sessions with reinforcement learning that rewards correctness while discouraging successful lower-effort attempts from exceeding the reasoning used by successful higher-effort attempts on the same task. The resulting model, Pi, showed ordered increases in both reasoning expenditure and performance from low to medium to xhigh settings across Terminal-Bench 2.1, GPQA Diamond, and SciCode, although some gains and costs varied by benchmark. On Terminal-Bench, Pi’s medium setting matched the base model’s xhigh pass rate with about 41% fewer output tokens, while xhigh achieved the strongest score; it also improved results across all SciCode effort levels and reached its best GPQA score at xhigh. The report emphasizes verifier and training-pipeline quality through a separate QA process for tasks, notes that validation loss and quantization perplexity did not reliably predict agent performance, and releases BF16, FP8, and multiple GGUF quantized formats for deployment. Limitations include a single, budget-constrained RL run, small checkpoint and quantization evaluations, incomplete task auditing, and the inability to isolate the specific contribution of the reinforcement-learning reward from the broader training process.

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