Reward-Generation Gap Blog Design

Goal

Create an English long-form publication page for “Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms” that matches the tone and readability of the existing SAGO blog post while remaining accessible to a general ML audience.

Audience

• General ML readers
• Researchers familiar with LLM alignment at a high level
• Readers who want intuition first and details second

Style Reference

Use the structure and pacing of _publications/2025-04-30-ar-checker.md:

• short opening paragraph
• problem framing
• key insight
• method explanation
• result walkthrough with figures
• concise takeaway
• citation block

Content Structure

1. Opening paragraph introducing the paper and positioning it as an accessible overview
2. The Problem: Direct Alignment Still Misses Something
3. Our Key Insight: Sequence-Level Rewards Do Not Guarantee Good Prefixes
4. Method: Prefix-Oriented Equal-length Training (POET)
5. Why This Makes Sense
6. Results: Better Alignment Across Settings
7. When Does POET Help Most?
8. Takeaway
9. Citation block

Figure Plan

• intro_issue: first hero-style figure to explain the reward-generation gap intuitively
• delta_means: support the claim that prefix quality differences emerge early
• prefix_quality: show that POET improves the quality of generated prefixes
• one compact results summary in text/table rather than overloading the page with dense benchmark visuals

Constraints

• Keep the post close in length and density to the SAGO blog
• Use fewer formulas than the SAGO page
• Avoid a theory-first presentation
• Keep the markdown compatible with the existing Jekyll publication page setup