Division by Zero in GPTRewardModel with Empty Batches

### 🐛 Describe the bug

### **Summary**
The `GPTRewardModel.forward()` method crashes with a division by zero error when processing batches with 0 or 1 samples.

---

### **Environment**
- **File**: `examples/summarize_rlhf/reward_model/reward_model.py`
- **Class**: `GPTRewardModel`
- **Method**: `forward()`

---

### **Problem Description**

When the input batch has 0 or 1 samples, the batch size calculation results in `bs = 0`:

```python
bs = input_ids.shape[0] // 2 # If shape[0] = 1, then bs = 0
```

This causes **two critical crashes**:

#### **Crash #1: Division by Zero (Line 100)**
```python
loss = loss / bs # ZeroDivisionError when bs = 0
```

#### **Crash #2: Empty Tensor Stack (Lines 103-104)**
```python
chosen_end_scores = torch.stack(chosen_end_scores) # RuntimeError: stack expects non-empty list
rejected_end_scores = torch.stack(rejected_end_scores)
```

---

### **Reproduction Steps**

```python
from reward_model import GPTRewardModel
import torch

model = GPTRewardModel("EleutherAI/gpt-j-6B")

# Test with single sample (bs will be 0)
input_ids = torch.randint(0, 50000, (1, 512))
output = model(input_ids) # 💥 CRASH
```

**Error output:**
```
ZeroDivisionError: division by zero
at line 100: loss = loss / bs
```

---

### **Root Cause**

The model expects paired inputs (chosen + rejected) and splits them:
- Input shape: `(batch_size, seq_len)`
- After split: `bs = batch_size // 2`
- **If batch_size ∈ {0, 1}**: `bs = 0` → crash

The code doesn't handle the edge case where splitting results in zero samples per category.

---

### **Proposed Solution**

Add an early return guard after the batch size calculation:

```python
bs = input_ids.shape[0] // 2

# Handle empty batch edge case
if bs == 0:
return {
"loss": torch.tensor(0.0, device=input_ids.device),
"chosen_end_scores": torch.tensor([], device=input_ids.device),
"rejected_end_scores": torch.tensor([], device=input_ids.device),
}
```

**Benefits:**
✅ Prevents division by zero
✅ Prevents empty tensor stack errors
✅ Returns consistent output format
✅ Maintains proper device placement
✅ Backward compatible (no breaking changes)

---

### **Impact**

**Severity**: Medium-High
- Crashes training/inference on edge cases
- May occur during:
- Dataset iteration with uneven batches
- Distributed training with small batch sizes
- Testing/debugging with single samples

**Affected Users**: Anyone using GPTRewardModel with small or variable batch sizes

**Severity**: Medium-High
- Crashes training/inference on edge cases
- May occur during:
- Dataset iteration with uneven batches
- Distributed training with small batch sizes
- Testing/debugging with single samples

**Affected Users**: Anyone using GPTRewardModel with small or variable batch sizes

---

### **Additional Context**

The fix is straightforward and adds only 10 lines of defensive code. I have the implementation ready and tested if you'd like me to submit a PR.

**Affected code location:**
```
examples/summarize_rlhf/reward_model/reward_model.py
Lines 51-57
```

---

### **Checklist**
- [x] Bug identified and root cause analyzed
- [x] Reproduction steps provided
- [x] Solution proposed with code snippet
- [x] Impact assessment completed

### Which trlX version are you using?

latest

### Additional system and package information

Python=3.12.3, transformers=4.57.1,NVIDIA GB10, 580.95.05, CUDA=12.1