docs: add final submission readme and training logs

Document the environment, training evidence, model links, authors, plots, and remaining finale checklist for hackathon submission review.

Made-with: Cursor

README.md

@@ -13,11 +13,15 @@ license: mit
 
 > OpenEnv Hackathon | Meta × HuggingFace × PyTorch
 
-An RL environment for training **epistemic robustness** in LLMs — teaching models to identify corrupted documents and resist misleading retrieved context.
+An OpenEnv RL environment for training **epistemic robustness** in LLMs: agents must answer factual questions while identifying corrupted retrieved documents.
+
+**Authors:** Siddh Sanghavi, Aagam Parekh
 
 ## The Problem
 
-LLMs fine-tuned with RLHF increasingly defer to retrieved documents even when those documents contradict the model's own (correct) parametric knowledge. Standard retrieval-augmented generation makes this worse. We train a model to cross-reference claims, flag corrupted sources, and answer from reliable knowledge.
+RAG systems often make LLMs over-trust retrieved text, even when the retrieved context is corrupted. ContextCorruption-Env turns that failure mode into a trainable task: the model sees a realistic multi-document QA context, some documents contain plausible falsehoods, and the agent must both answer correctly and flag the unreliable sources.
+
+This fits **Theme #3.1: World Modeling / Professional Tasks**. The agent is operating in a partially observable information world where source reliability matters, and the reward teaches belief updating instead of blind context copying.
 
 ## The Environment
 
@@ -25,6 +29,14 @@ LLMs fine-tuned with RLHF increasingly defer to retrieved documents even when th
 - **4 corruption levels**: number mutation → entity swap → semantic inversion → coherent fabrication
 - **450 QA facts** from Natural Questions + PopQA
 - **Deterministic reward**, no LLM judge
+- **OpenEnv-compatible HTTP API** hosted on Hugging Face Spaces
+
+### Actions
+
+- `read_doc`: spend budget to inspect a document
+- `flag_suspicious`: mark a document as corrupted
+- `unflag_doc`: remove a flag
+- `submit_answer`: finish the episode with answer and confidence
 
 ### Reward Signal
 
@@ -43,13 +55,19 @@ LLMs fine-tuned with RLHF increasingly defer to retrieved documents even when th
 | Agent | Avg Reward | Answer Acc | Corruption Detection |
 |---|---|---|---|
 | Random baseline | 0.1302 | ~0% | ~25% |
-| Qwen2-1.5B GRPO | _after training_ | — | — |
+| Qwen2-1.5B GRPO | 0.3289 final logged reward | Training run evidence | Training run evidence |
+
+The finished WandB run shows reward improving above the random baseline during GRPO training. The pushed checkpoint is also loaded inside the environment Space through `/model/infer` for a live sanity check.
+
+![Reward curve](assets/reward_curve.png)
+
+![Loss curve](assets/loss_curve.png)
 
 ## Training
 
 ```bash
-# Colab (A100 recommended)
-# Open training/ContextCorruption_GRPO.ipynb
+# Training Space / A100
+python -m training.space_runner
 ```
 
 Training script: [`training/train_grpo.py`](training/train_grpo.py)
@@ -57,21 +75,60 @@ Notebook: [`training/ContextCorruption_GRPO.ipynb`](training/ContextCorruption_G
 
 ## Links
 
-- **HF Space (env server):** https://huggingface.co/spaces/Siddh12334/context-corruption-env
-- **Colab notebook:** _add link after training_
-- **WandB run:** _add link after training_
-- **Blog post:** _add link_
+- **HF Space (environment + model inference):** https://huggingface.co/spaces/Siddh12334/context-corruption-env
+- **Training Space:** https://huggingface.co/spaces/Siddh12334/context-corruption-training
+- **Trained LoRA checkpoint:** https://huggingface.co/Siddh12334/qwen-1.5b-context-corruption
+- **WandB finished run:** https://wandb.ai/siddh230505-animeta/context-corruption-env/runs/rl5jygl8
+- **Training logs/history:** [`assets/training_history_rl5jygl8.csv`](assets/training_history_rl5jygl8.csv)
+- **Colab notebook:** `training/ContextCorruption_GRPO.ipynb`
+- **Mini-blog/video/slides:** TODO before final submission
+
+## Training Logs
+
+The final GRPO run is logged in WandB at:
+
+https://wandb.ai/siddh230505-animeta/context-corruption-env/runs/rl5jygl8
+
+For offline review, the exported scalar history is committed at [`assets/training_history_rl5jygl8.csv`](assets/training_history_rl5jygl8.csv). The key plotted metrics are embedded above as `reward_curve.png` and `loss_curve.png`.
 
 ## Quick Start
 
 ```python
-from openenv.core import SyncEnvClient
+import requests
 
-client = SyncEnvClient(base_url="https://Siddh12334-context-corruption-env.hf.space")
-obs = client.reset()
+base = "https://siddh12334-context-corruption-env.hf.space"
+
+print(requests.get(base + "/health").json())
+episode = requests.post(base + "/reset", json={}).json()
+obs = episode["observation"]
 print(obs["question"])
+
+action = {
+    "action": {
+        "action_type": "submit_answer",
+        "answer": "unknown",
+        "confidence": 0.5,
+    }
+}
+print(requests.post(base + "/step", json=action).json())
 ```
 
+### Optional Model Inference
+
+The environment Space also exposes the trained checkpoint for live sanity checks:
+
+```python
+response = requests.post(base + "/model/infer", json={"observation": obs}).json()
+print(response["text"])
+```
+
+Verified status:
+
+- `/health` returns healthy
+- `/reset` returns 8 documents and budget 12
+- `/model/status` reports A100 GPU and the trained checkpoint
+- `/model/infer` lazy-loads `Siddh12334/qwen-1.5b-context-corruption`
+
 ## Repo Structure
 
 ```
@@ -79,9 +136,23 @@ environment/   # OpenEnv-compliant env (actions, reward, env, server)
 data/          # QA loader, corruption functions, document generator
 training/      # GRPO training script + Colab notebook
 eval/          # Baseline evaluation
-assets/        # reward_curve.png, loss_curve.png (after training)
+assets/        # reward_curve.png, loss_curve.png
 ```
 
+## Final Checklist
+
+- [x] OpenEnv environment implemented with `reset`, `step`, `state`, schemas, and reward
+- [x] Environment hosted on Hugging Face Spaces
+- [x] Baseline evaluation saved in `eval/baseline_results.json`
+- [x] GRPO training script using Unsloth / TRL
+- [x] Trained checkpoint pushed to Hugging Face Hub
+- [x] Reward and loss plots committed
+- [x] Training logs linked and exported
+- [x] Environment Space verified healthy after deployment
+- [x] Trained checkpoint verified through `/model/infer`
+- [ ] Add mini-blog, video, or slides link
+- [ ] Replace TODO link above before final submission
+
 ## Citation
 
 ```bibtex

assets/training_history_rl5jygl8.csv

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openenv.yaml

@@ -4,7 +4,7 @@ description: >
   OpenEnv environment for training epistemic robustness in LLMs.
   Agents identify correct answers and flag corrupted documents
   in a multi-doc QA setting with verifiable, objective rewards.
-author: "Your Team Name"
+author: "Siddh Sanghavi, Aagam Parekh"
 license: MIT
 
 environment: