## Line Graphs: Performance Metrics Across Training Steps
### Overview
The image contains three columns of line graphs comparing performance metrics across three training methods (REINFORCE, GRPO, REC-TwoSides, REC-RingNoIS) under different synchronization settings ("sync interval = 20", "sync offset = 10", "offline"). Each column contains four subplots: Evaluation Accuracy, Training Reward, KL Divergence, and Clipping Fraction. The x-axis represents training steps (0–150), while y-axes vary by subplot.
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### Components/Axes
- **Columns**:
- Left: "sync interval = 20" (blue dashed line)
- Middle: "sync offset = 10" (orange dashed line)
- Right: "offline" (no dashed line)
- **Subplots**:
1. **Evaluation Accuracy**: Y-axis = 0.0–1.0
2. **Training Reward**: Y-axis = 0.0–1.0
3. **KL Divergence**: Y-axis = 10–100
4. **Clipping Fraction**: Y-axis = 10–100
- **Legends**: Positioned at the bottom of each column, with colors:
- Blue: REINFORCE
- Green: GRPO
- Orange: REC-TwoSides
- Purple: REC-RingNoIS
- Dashed Orange: REC-TwoSides (0.2) & (0.6, 2.0)
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### Detailed Analysis
#### **Column 1: sync interval = 20**
1. **Evaluation Accuracy**:
- Blue (REINFORCE): Starts at ~0.6, dips to ~0.4 at 50 steps, then stabilizes.
- Green (GRPO): Flat at ~0.6.
- Orange (REC-TwoSides): Peaks at ~0.8 at 100 steps, then declines.
- Purple (REC-RingNoIS): Smooth increase from ~0.4 to ~0.8.
- Dashed Orange: Peaks at ~0.7 at 100 steps.
2. **Training Reward**:
- Blue: Drops sharply to ~0.2 at 50 steps, then stabilizes.
- Green: Flat at ~0.6.
- Orange: Peaks at ~0.8 at 100 steps.
- Purple: Stable at ~0.7.
- Dashed Orange: Peaks at ~0.7 at 100 steps.
3. **KL Divergence**:
- Blue: Fluctuates between 10–20.
- Green: Stable at ~10.
- Orange: Spikes to ~30 at 100 steps.
- Purple: Stable at ~10.
- Dashed Orange: Spikes to ~30 at 100 steps.
4. **Clipping Fraction**:
- Blue: Fluctuates between 10–20.
- Green: Stable at ~10.
- Orange: Increases to ~20 at 100 steps.
- Purple: Stable at ~10.
- Dashed Orange: Increases to ~20 at 100 steps.
#### **Column 2: sync offset = 10**
1. **Evaluation Accuracy**:
- Blue: Starts at ~0.5, dips to ~0.3 at 50 steps, then stabilizes.
- Green: Flat at ~0.6.
- Orange: Peaks at ~0.7 at 100 steps.
- Purple: Smooth increase from ~0.5 to ~0.8.
- Dashed Orange: Peaks at ~0.6 at 100 steps.
2. **Training Reward**:
- Blue: Drops to ~0.3 at 50 steps, then stabilizes.
- Green: Flat at ~0.6.
- Orange: Peaks at ~0.7 at 100 steps.
- Purple: Stable at ~0.7.
- Dashed Orange: Peaks at ~0.6 at 100 steps.
3. **KL Divergence**:
- Blue: Fluctuates between 10–20.
- Green: Stable at ~10.
- Orange: Spikes to ~25 at 100 steps.
- Purple: Stable at ~10.
- Dashed Orange: Spikes to ~25 at 100 steps.
4. **Clipping Fraction**:
- Blue: Fluctuates between 10–20.
- Green: Stable at ~10.
- Orange: Increases to ~15 at 100 steps.
- Purple: Stable at ~10.
- Dashed Orange: Increases to ~15 at 100 steps.
#### **Column 3: offline**
1. **Evaluation Accuracy**:
- Blue: Starts at ~0.4, dips to ~0.2 at 50 steps, then stabilizes.
- Green: Flat at ~0.6.
- Orange: Peaks at ~0.7 at 100 steps.
- Purple: Smooth increase from ~0.5 to ~0.8.
- Dashed Orange: Peaks at ~0.6 at 100 steps.
2. **Training Reward**:
- Blue: Drops to ~0.2 at 50 steps, then stabilizes.
- Green: Flat at ~0.6.
- Orange: Peaks at ~0.7 at 100 steps.
- Purple: Stable at ~0.7.
- Dashed Orange: Peaks at ~0.6 at 100 steps.
3. **KL Divergence**:
- Blue: Fluctuates between 10–20.
- Green: Stable at ~10.
- Orange: Spikes to ~20 at 100 steps.
- Purple: Stable at ~10.
- Dashed Orange: Spikes to ~20 at 100 steps.
4. **Clipping Fraction**:
- Blue: Fluctuates between 10–20.
- Green: Stable at ~10.
- Orange: Increases to ~10 at 100 steps.
- Purple: Stable at ~10.
- Dashed Orange: Increases to ~10 at 100 steps.
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### Key Observations
1. **REC-RingNoIS (Purple)** consistently shows the highest Evaluation Accuracy and Training Reward across all settings, with smooth trends.
2. **REC-TwoSides (Orange)** exhibits the highest KL Divergence and Clipping Fraction, indicating greater exploration but potential instability.
3. **REINFORCE (Blue)** performs poorly in Training Reward and Evaluation Accuracy, with erratic trends.
4. **GRPO (Green)** maintains stable metrics across all settings, suggesting robustness.
5. The dashed orange line (REC-TwoSides variants) shows intermediate performance, combining traits of REC-TwoSides and REC-RingNoIS.
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### Interpretation
The data suggests that synchronization settings significantly impact performance:
- **Sync Interval = 20** and **Sync Offset = 10** allow REC-RingNoIS to outperform others, likely due to balanced exploration/exploitation.
- **Offline** settings degrade REINFORCE's performance, highlighting its reliance on synchronization.
- REC-TwoSides variants (dashed orange) show trade-offs: higher KL Divergence/Clipping Fraction (exploration) but lower Evaluation Accuracy/Training Reward (stability).
- GRPO's stability across all settings implies it is less sensitive to synchronization parameters, making it a reliable baseline.
Notably, REC-RingNoIS achieves the best balance of high Evaluation Accuracy and low KL Divergence, suggesting it optimizes policy updates effectively. The dashed orange line's intermediate performance indicates that combining REC-TwoSides variants may mitigate some instability while retaining exploration benefits.
