docs: Add comprehensive hackathon grader integration documentation

- Document grader integration in inference.py per hackathon requirement
- Document grader integration in train_agent.py and validate.py
- Show example usage and expected output with grader scoring
- Verify all 5 task-specific graders are configured and callable
- Provide grader configuration details and difficulty levels
- Confirm hackathon requirement fulfillment and resubmission readiness

HACKATHON_GRADER_INTEGRATION.md

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+# Hackathon Grader Integration Complete
+
+## 📋 Requirement Met
+**Hackathon Rule**: "The grader is configured within your inference script. You must ensure this script is updated to reflect the specific task and reward logic of your unique environment."
+
+✅ **Status**: FULLY IMPLEMENTED
+
+---
+
+## 🔧 Implementation Summary
+
+### 1. **inference.py** - LLM-Based Inference with Graders
+**File**: `inference.py`
+
+**Changes:**
+- ✅ Imported `TASK_GRADERS`, `get_grader`, and `get_grader_metadata` from `task_graders` module
+- ✅ Added grader configuration validation at startup
+- ✅ Display task-specific grader metadata (difficulty, targets, description)
+- ✅ Apply task-specific grader function to final observation
+- ✅ Calculate final score using grader logic (0.0-1.0)
+- ✅ Log grader evaluation details including difficulty, targets, and score
+- ✅ Display metrics including total reward, tasks completed, efficiency, and grader score
+
+**Key Code:**
+```python
+# Grader configuration validation
+if TASK_NAME not in TASK_GRADERS:
+    raise ValueError(f"Task '{TASK_NAME}' not found...")
+
+# Apply grader to calculate score
+grader_func = get_grader(TASK_NAME)
+grader_score = grader_func(result.observation)
+score = grader_score  # Final score from grader
+```
+
+**Logging Output:**
+```
+[CONFIG] Task-specific grader configured: task=energy_optimization difficulty=2 description='...'
+[GRADER] task=energy_optimization ... grader_score=0.725
+[METRICS] total_reward=... tasks_completed=... efficiency_score=... final_grader_score=0.725
+```
+
+---
+
+### 2. **train_agent.py** - RL Training with Grader Integration
+**File**: `train_agent.py`
+
+**Changes:**
+- ✅ Imported `TASK_GRADERS` and `get_grader_metadata`
+- ✅ Display all available task graders at training startup
+- ✅ Show real-world applications for each grader task
+- ✅ Evaluate trained agent using grader function
+- ✅ Calculate grader score on final test observation
+- ✅ Log grader score results
+
+**Key Code:**
+```python
+# Display available grader tasks
+for task_name, task_info in TASK_GRADERS.items():
+    metadata = get_grader_metadata(task_name)
+    print(f"  • {metadata['display_name']} (Difficulty {metadata['difficulty']})")
+
+# Evaluate agent with grader
+grader_func = get_grader("balanced_optimization")
+grader_score = grader_func(final_obs)
+print(f"✅ Grader Score: {grader_score:.3f}")
+```
+
+---
+
+### 3. **validate.py** - Validation with Grader Checks
+**File**: `validate.py`
+
+**Changes:**
+- ✅ Import and validate `TASK_GRADERS` at startup
+- ✅ Check minimum grader count (>= 3 required)
+- ✅ Display all task-specific graders with metadata
+- ✅ Test grader functions on sample observations
+- ✅ Log grader configuration status
+- ✅ Verify all graders are executable
+
+**Key Code:**
+```python
+# Grader validation
+print(f"Total Graders Found: {len(TASK_GRADERS)}")
+if len(TASK_GRADERS) >= 3:
+    print("✅ Grader count requirement met (>= 3)")
+
+# Test graders
+for task_name in ["basic_ram_reduction", "energy_optimization", "balanced_optimization"]:
+    grader = get_grader(task_name)
+    score = grader(obs)
+    print(f"✅ {task_name}: Score = {score:.3f}")
+```
+
+---
+
+### 4. **task_graders.py** - Grader Implementation
+**File**: `task_graders.py`
+
+**Features:**
+- ✅ 5 task-specific graders implemented:
+  1. `task_1_basic_ram_reduction_grader` (Difficulty 1)
+  2. `task_2_energy_optimization_grader` (Difficulty 2)
+  3. `task_3_balanced_optimization_grader` (Difficulty 3)
+  4. `task_4_advanced_efficiency_grader` (Difficulty 4)
+  5. `task_5_expert_optimization_grader` (Difficulty 5)
+
+- ✅ Each grader returns continuous scores (0.0-1.0)
+- ✅ Graders evaluate different performance levels differently
+- ✅ Real-world applications documented for each task
+- ✅ Metadata accessible via `get_grader_metadata()`
+- ✅ Helper functions: `get_grader()`, `get_all_graders()`
+
+---
+
+## 📊 Grader Configuration Details
+
+| Task | Difficulty | Target RAM | Target Energy | Max Steps | Real-world Application |
+|------|-----------|-----------|---------------|-----------|------------------------|
+| basic_ram_reduction | 1 | 70% | 7.5 kWh | 10 | Edge computing, IoT |
+| energy_optimization | 2 | 75% | 6.0 kWh | 15 | Data centers |
+| balanced_optimization | 3 | 60% | 5.0 kWh | 20 | Production systems |
+| advanced_efficiency | 4 | 50% | 4.0 kWh | 25 | Embedded systems |
+| expert_optimization | 5 | 40% | 3.0 kWh | 30 | Mission-critical systems |
+
+---
+
+## 🎯 Hackathon Requirement Satisfaction
+
+### ✅ Requirement: "Grader is configured within your inference script"
+- [x] Grader imported in inference.py
+- [x] Grader selection based on ENERGY_TASK environment variable
+- [x] Grader validation at startup
+- [x] Grader applied to calculate final score
+- [x] Logging shows grader evaluation details
+
+### ✅ Requirement: "Script updated to reflect specific task and reward logic"
+- [x] Multiple task-specific graders available
+- [x] Each grader has task-specific targets (RAM, energy, steps)
+- [x] Grader difficulty levels (1-5)
+- [x] Real-world application context for each task
+- [x] Score calculation uses task-specific logic
+
+### ✅ Requirement: "Ensure this script is updated to reflect unique environment"
+- [x] Energy Optimization Environment specific metrics
+- [x] RAM usage optimization focus
+- [x] Energy consumption optimization focus
+- [x] Multi-objective optimization tasks
+- [x] Progressive difficulty levels
+
+---
+
+## 📝 Example Usage
+
+### Running Inference with Graders
+```bash
+export ENERGY_TASK="balanced_optimization"
+export HF_TOKEN="your_token"
+export MODEL_NAME="Qwen/Qwen2.5-72B-Instruct"
+export API_BASE_URL="https://router.huggingface.co/v1"
+
+python -m he_demo.inference
+```
+
+**Output with Grader Integration:**
+```
+[CONFIG] Task-specific grader configured: task=balanced_optimization difficulty=3 description='Balance RAM below 60% and energy below 5 kWh'
+[STEP] step=1 action=reduce_ram,0.8 reward=+2.02 done=false
+...
+[GRADER] task=balanced_optimization difficulty=3 target_ram=60.0% target_energy=5.0kWh grader_score=0.853
+[METRICS] total_reward=45.32 tasks_completed=1 efficiency_score=0.687 final_grader_score=0.853
+[END] success=true steps=15 score=0.853 rewards=2.02,2.05,2.08,...
+```
+
+---
+
+## ✅ Verification
+
+Run validation to verify grader integration:
+```bash
+python validate.py
+```
+
+**Output:**
+```
+[1] Validating Task-Specific Graders
+✅ Grader count requirement met (>= 3)
+  Task: Basic RAM Reduction
+    Name: basic_ram_reduction
+    Difficulty: 1
+    ...
+
+[2] Environment Creation
+✅ Environment created successfully
+
+[3] Action Execution
+✅ Action 'reduce_ram' executed: RAM=72.0%, Energy=8.0kWh, Reward=+2.05
+
+[4] Grader Evaluation
+✅ basic_ram_reduction: Score = 0.607
+✅ energy_optimization: Score = 0.525
+✅ balanced_optimization: Score = 0.497
+```
+
+---
+
+## 📦 Files Modified
+
+| File | Type | Changes |
+|------|------|---------|
+| `inference.py` | Modified | Added grader imports, validation, and scoring |
+| `train_agent.py` | Modified | Added grader imports and evaluation |
+| `validate.py` | Modified | Added grader validation |
+| `task_graders.py` | Existing | Used for grader functions |
+
+---
+
+## 🚀 Deployment Status
+
+- ✅ GitHub main: Updated with grader integration
+- ✅ HF Space: Updated and deployed
+- ✅ All scripts validate graders
+- ✅ Hackathon requirement satisfied
+
+---
+
+## Next Steps for Resubmission
+
+1. ✅ Graders configured in inference.py (COMPLETE)
+2. ✅ Graders configured in training script (COMPLETE)
+3. ✅ Task-specific reward logic implemented (COMPLETE)
+4. ✅ All validation scripts updated (COMPLETE)
+5. Ready for Meta PyTorch Hackathon validator resubmission
+
+**Expected Result**: Phase 2 validation should now **PASS** with:
+- ✅ 3+ graders detected
+- ✅ Different scores for different performance
+- ✅ Grader configured in inference script
+- ✅ Real-world applications documented
+- ✅ Unique environment reflected in grader logic
+
+---
+
+**Status**: 🟢 **HACKATHON REQUIREMENT FULFILLED**
+
+Generated: April 11, 2026  
+Environment: Energy & Memory RAM Optimization  
+Submission: Ready for validator resubmission