Use public WandB report wording consistently

training/grpo_colab.ipynb

@@ -36,7 +36,7 @@
     "| **baseline_7b_scores.json** (apples-to-apples baseline) | [`../baseline_7b_scores.json`](../baseline_7b_scores.json) |\n",
     "| **baseline_72b_v2_scores.json** (size-baseline) | [`../baseline_72b_v2_scores.json`](../baseline_72b_v2_scores.json) |\n",
     "\n",
-    "**Why no embedded cell outputs?** Cell outputs from a real GRPO run on a 7B model would balloon the notebook to >5 MB (the spec asks for small Hugging Face Space repo sizes). Instead we ship: the public WandB run for live metrics, the reward-curve PNG and full log in `results/`, and the trained adapter on HuggingFace Hub. Anyone can verify the numbers without rerunning.\n",
+    "**Why no embedded cell outputs?** Cell outputs from a real GRPO run on a 7B model would balloon the notebook to >5 MB (the spec asks for small Hugging Face Space repo sizes). Instead we ship: the public WandB report for live metrics, the reward-curve PNG and full log in `results/`, and the trained adapter on HuggingFace Hub. Anyone can verify the numbers without rerunning.\n",
     "\n",
     "**To reproduce the run yourself**: open this notebook in [Colab](https://colab.research.google.com/) \u2192 set `HF_TOKEN` and `WANDB_API_KEY` in the Secrets pane \u2192 Runtime \u2192 Run all. ~45 min on a T4. The HF Jobs path (~$2 on L4, ~2 hours) is documented in [`README.md`](README.md).\n"
    ]