Add training logs and fix gitignore

logs/README.md

@@ -0,0 +1,49 @@
+# Training Logs
+
+This directory contains output from GRPO training runs against the live DataCentricEnvironment.
+
+## Files
+
+### `training.jsonl`
+Per-episode reward log. Each line is one training episode:
+
+```json
+{
+  "episode": 5,
+  "task": "task_1_easy",
+  "level": 1,
+  "reward": 0.312,
+  "accuracy_gain": 0.091,
+  "steps_used": 11,
+  "success": true,
+  "curriculum_stage": "easy"
+}
+```
+
+| Field | Description |
+|---|---|
+| `episode` | Global episode counter across the training run |
+| `task` | Which curriculum task was run (`task_0_tutorial` … `task_3_hard`) |
+| `level` | Curriculum level (0=tutorial, 1=easy, 2=medium, 3=hard) |
+| `reward` | Total episode reward from the composable rubric system [-1.0, 1.0] |
+| `accuracy_gain` | Raw accuracy improvement above the episode baseline |
+| `steps_used` | Number of actions taken before submit |
+| `success` | Whether the agent hit the target accuracy threshold |
+| `curriculum_stage` | Human-readable level label |
+
+### `grpo/` and `sft/`
+TensorBoard event files. View with:
+```bash
+tensorboard --logdir logs/
+```
+
+## Generating Real Logs
+
+Run the training notebook:
+```
+train_colab.ipynb  →  Step 7 (GRPO Training)
+```
+
+The log is written incrementally — one line per episode — by `log_episode_jsonl()` in `train_data_centric.py`. After training, commit the full `logs/training.jsonl` to replace this sample file.
+
+> **Note:** The `training.jsonl` in this directory is a **sample** showing the log format and expected learning trajectory. Replace it with your actual run output after training completes.