Elo

Overview

elo is a feedback-driven selection algorithm that ranks candidate models using an Elo-style rating system based on pairwise comparisons.

It aligns to config/algorithm/selection/elo.yaml.

Paper: RouteLLM: Simple and Effective LLM Routing — uses the Bradley-Terry model for pairwise preference learning.

Key Advantages

• Reuses historical pairwise feedback instead of only current-request heuristics.
• Ratings improve over time as more comparisons arrive (online learning).
• Supports category-aware weighting for routes with distinct workloads.
• Configurable time decay to gradually forget stale comparisons.
• Optional cost-aware selection to balance quality vs. price.

Algorithm Principle

Elo rating is built on the Bradley-Terry model, which estimates the probability that model A is preferred over model B:

$P(A \succ B) = \frac{1}{1 + 10^{(R_B - R_A) / 400}}$

After each pairwise comparison, ratings are updated:

$R_A' = R_A + K \cdot (S_A - E_A)$

Where:

• $K$ is the learning rate (k_factor, default 32)
• $S_A$ is the actual outcome (1 = win, 0 = loss, 0.5 = tie)
• $E_A = P(A \succ B)$ is the expected score

When category_weighted is enabled, each decision maintains independent per-category ratings, so a model's performance in "math" doesn't affect its "coding" rating.

Select Flow

Feedback Flow

Feedback API

Submit pairwise feedback to update Elo ratings:

curl -X POST http://localhost:8000/api/v1/feedback \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Solve: 2x + 5 = 15",
    "winner_model": "gpt-4",
    "loser_model": "llama-3.2-3b",
    "decision_name": "math_reasoning"
  }'

Feedback fields:

Field	Required	Description
query	Yes	Original query text
winner_model	Yes	Preferred model name
loser_model	No	Rejected model (for pairwise)
tie	No	Both models performed equally
decision_name	No	Category context for category-weighted Elo
user_id	No	User identifier for per-user tracking
confidence	No	Feedback confidence (0.0-1.0)

What Problem Does It Solve?

When model quality shifts over time, static priority lists and one-shot heuristics become stale. elo lets the router learn from pairwise feedback so candidate ranking reflects observed wins instead of frozen assumptions.

When to Use

• You collect route-level feedback or quality comparisons.
• Ranking should improve over time as more comparisons arrive.
• One route sees repeatable workloads where a rating system is useful.
• You want online learning without retraining.

Known Limitations

• Requires sufficient pairwise comparisons before ratings stabilize (controlled by min_comparisons).
• Cold start: new models begin at initial_rating regardless of actual capability.
• No semantic understanding of queries — purely feedback-driven.

Configuration

algorithm:
  type: elo
  elo:
    initial_rating: 1500          # Starting rating for new models
    k_factor: 32                  # Learning rate (higher = more volatile)
    category_weighted: true       # Per-category Elo ratings
    decay_factor: 0.0             # Time decay for old comparisons (0 = none)
    min_comparisons: 5            # Minimum comparisons before rating is stable
    cost_scaling_factor: 0.0      # Cost consideration (0 = ignore)
    storage_path: /var/lib/vsr/elo_ratings.json  # Persist ratings
    auto_save_interval: 1m        # Auto-save frequency

Parameters

Parameter	Type	Default	Description
initial_rating	float	1500	Starting Elo rating for new models
k_factor	float	32	Rating volatility (range 1–100)
category_weighted	bool	true	Enable per-category ratings
decay_factor	float	0.0	Time decay for old comparisons (0–1)
min_comparisons	int	5	Minimum comparisons for stable rating
cost_scaling_factor	float	0.0	Cost penalty per $1M tokens (0 = ignore)
storage_path	string	—	File path to persist Elo ratings
auto_save_interval	string	1m	Auto-save interval (e.g. 5m, 30s)