server/reward_evolution.py

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patheffects import withStroke

# Actual Llama-3.1-8b-instant Convergence Trajectories
# Step 0 represents standard/naive reasoning without environment diagnostic feedback.
# Step 1 represents the immediate performance jump after RLVR In-Context Adaptation.
steps = [0, 1]
score_easy = [0.12, 0.94]
score_med = [0.12, 1.00]
score_hard = [0.12, 0.90]

colors = {
    'easy': '#00F5FF',    # Cyan
    'medium': '#FF00E5',  # Magenta
    'hard': '#FFD700',    # Gold
    'bg': '#0D0F14',      # Obsidian
    'grid': '#1A1D23',
    'text': '#E0E0E0'
}

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(10, 6), dpi=300)
fig.patch.set_facecolor(colors['bg'])
ax.set_facecolor(colors['bg'])

def plot_trajectory(x, y, label, color, marker):
    # Glow layers
    for w in range(1, 12, 2):
        ax.plot(x, y, color=color, linewidth=w, alpha=0.03, zorder=2)
    # Main line
    ax.plot(x, y, marker=marker, markersize=10, linewidth=3.5, 
            label=label, color=color, zorder=5, alpha=1.0,
            path_effects=[withStroke(linewidth=3, foreground='black')])

# Plotting
plot_trajectory(steps, score_easy, 'Task Easy (Clarification)', colors['easy'], 'o')
plot_trajectory(steps, score_med, 'Task Medium (New Rule)', colors['medium'], 's')
plot_trajectory(steps, score_hard, 'Task Hard (Evolution Trade-offs)', colors['hard'], 'D')

# Strategic Annotations
ax.annotate(' Naive Proposal\n (Vague / Implicit)', xy=(0, 0.12), xytext=(-0.1, 0.3),
             arrowprops=dict(facecolor='#FF5252', shrink=0.05, width=1, headwidth=6),
             fontsize=11, fontweight='bold', color='#FF5252', bbox=dict(facecolor='#000', alpha=0.5))

ax.annotate(' RLVR In-Context\n Adaptation', xy=(1, 0.94), xytext=(0.6, 0.5),
             arrowprops=dict(facecolor='#00FF00', shrink=0.05, width=1, headwidth=6),
             fontsize=11, fontweight='bold', color='#00FF00', bbox=dict(facecolor='#000', alpha=0.5))

ax.set_title('PolicyEvolverEnv: Strategic Governance Optimization', fontsize=18, fontweight='black', pad=25)
ax.set_xlabel('Environment Interaction Phase', fontsize=12, labelpad=10)
ax.set_ylabel('In-Context Grader Reward (0.0 to 1.0)', fontsize=12, labelpad=10)

ax.set_xticks(steps)
ax.set_xticklabels(['Naive Baseline', 'Optimized (0.90+ Tier)'])
ax.set_ylim(0, 1.1)
ax.grid(True, linestyle='-', color=colors['grid'], alpha=0.4, zorder=1)

# Style Overrides
for spine in ax.spines.values(): spine.set_visible(False)
legend = ax.legend(fontsize=11, loc='upper left', frameon=True, facecolor='#15181E', edgecolor='#2A2D35')
for text in legend.get_texts(): text.set_color(colors['text'])

# Branding
ax.text(0.98, 0.02, 'Llama-3.1-8b-instant | 100% Deterministic Reproducibility', 
        transform=ax.transAxes, ha='right', va='bottom', fontsize=9, alpha=0.6, color=colors['text'])

plt.tight_layout()
plt.savefig('reward_progression.png', dpi=300, facecolor=colors['bg'], bbox_inches='tight')
print("Updated High-Fidelity 0.9+ Chart Generated! 🚀📊")