RL tutorial: using Jev as reward model lifts Qwen reward from 0.583 to 0.759
sophiamyang · x · 2026-09-24
sophiamyang published a hands-on tutorial showing how to fine-tune Qwen3.8 27B with reinforcement learning on Fireworks, using the new model Jev as the reward model to reduce "AI slop."
- After 24 updates, mean Jev reward rose from 0.583 to 0.759, evidence of less AI-slop output
- Jev dramatically cuts RL cost and latency: 896 calls cost $0.061 with 186ms median latency
- Full runnable code tutorial included; author frames it as a toy example to get started with RL runs on Fireworks
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