RL Driven

Overview

rl_driven is a selection algorithm for online exploration and personalization. It supports multiple sub-modes: Thompson Sampling, Router-R1 (LLM-as-router), and Concurrent (arena mode).

It aligns to config/algorithm/selection/rl-driven.yaml.

Papers:

• Router-R1: Routing with Reinforcement Learning — multi-round RL routing with reward structure
• GMTRouter: Personalized LLM Router — GNN-based personalized routing (related)

Key Advantages

• Supports exploration instead of always exploiting the current best model.
• Thompson Sampling provides a principled exploration/exploitation balance.
• Router-R1 mode uses an LLM to reason about routing decisions.
• Per-user personalization adapts routing over time.
• Implicit feedback support (auto-detected satisfaction signals).

Sub-Modes

1. Thompson Sampling (default)

Uses Bayesian posterior sampling for exploration/exploitation. Each model's success probability is modeled as a Beta distribution $\text{Beta}(\alpha, \beta)$:

$\theta_m \sim \text{Beta}(\alpha_m, \beta_m)$

At each request, a sample is drawn for each candidate model, and the model with the highest sample is selected:

$m^* = \arg\max_m \theta_m$

After feedback, the distribution is updated:

• Win: $\alpha_m \leftarrow \alpha_m + 1$
• Loss: $\beta_m \leftarrow \beta_m + 1$
• Tie: $\alpha_m \leftarrow \alpha_m + 0.5,\ \beta_m \leftarrow \beta_m + 0.5$

When use_thompson_sampling: false, falls back to epsilon-greedy with exploration_rate as $\epsilon$.

2. Router-R1 (LLM-as-Router)

When enable_llm_routing: true, an external LLM server analyzes the query and selects the optimal model using "think" and "route" actions from the Router-R1 paper.

Reward structure: $R = R_{\text{format}} + (1-\alpha) \cdot R_{\text{outcome}} + \alpha \cdot R_{\text{cost}}$

Component	Description
$R_{\text{format}}$	-1 for incorrect format, 0 for correct
$R_{\text{outcome}}$	Based on exact match with ground truth
$R_{\text{cost}}$	Inversely proportional to model size × output tokens
$\alpha$	cost_reward_alpha — performance-cost tradeoff

3. Concurrent (Arena Mode)

When used as a looper algorithm (type: "concurrent"), executes all candidate models concurrently and aggregates results — useful for A/B testing and arena evaluation.

Select Flow (Thompson Sampling)

Feedback Flow

What Problem Does It Solve?

Online routing often needs exploration, adaptation, or personalization that static rankings cannot provide. rl_driven keeps that learning policy inside the router so model choice can improve from rewards and user feedback over time.

When to Use

• The route should keep exploring candidate models online.
• Personalization should adapt over time per user.
• Thompson Sampling is preferred when you want principled exploration without external dependencies.
• Router-R1 mode is preferred when you have a trained router LLM server.

Known Limitations

• Thompson Sampling requires sufficient samples before exploiting effectively (min_samples).
• Router-R1 LLM routing adds latency (extra LLM call per request).
• Router-R1 mode requires a separate trained router LLM server.
• Exploration incurs short-term cost for long-term gain.

Configuration

algorithm:
  type: rl_driven
  rl_driven:
    # Exploration control
    exploration_rate: 0.3              # Initial exploration rate (epsilon)
    exploration_decay: 0.99            # Decay per 100 selections
    min_exploration: 0.05              # Minimum exploration rate
    use_thompson_sampling: true        # Use Thompson Sampling vs epsilon-greedy

    # Personalization
    enable_personalization: true       # Per-user preference tracking
    personalization_blend: 0.7         # 1.0=fully personalized, 0.0=fully global
    session_context_weight: 0.5        # Within-session feedback weight
    implicit_feedback_weight: 0.5      # Auto-detected feedback weight

    # Cost awareness
    cost_awareness: true               # Prefer cheaper models for exploration
    cost_weight: 0.2                   # Cost influence weight

    # Persistence
    storage_path: /var/lib/vsr/rl_state.json
    auto_save_interval: 30s

    # Router-R1 reward
    use_router_r1_rewards: false       # Enable Router-R1 reward computation
    cost_reward_alpha: 0.3             # Performance-cost tradeoff in reward
    format_reward_penalty: -1.0        # Penalty for incorrect format

    # LLM-as-Router
    enable_llm_routing: false          # Enable LLM-based routing
    router_r1_server_url: ""           # Router-R1 server URL
    llm_routing_fallback: thompson     # Fallback when LLM routing fails

    # Multi-round
    enable_multi_round_aggregation: false

Parameters

Parameter	Type	Default	Description
exploration_rate	float	0.3	Initial exploration rate (0–1)
exploration_decay	float	0.99	Decay per 100 selections (0–1)
min_exploration	float	0.05	Minimum exploration rate (0–1)
use_thompson_sampling	bool	true	Thompson Sampling vs epsilon-greedy
enable_personalization	bool	true	Per-user preference tracking
personalization_blend	float	0.7	Global vs. personalized blend (0–1)
session_context_weight	float	0.5	Within-session feedback weight (0–1)
implicit_feedback_weight	float	0.5	Implicit feedback weight (0–1)
cost_awareness	bool	true	Prefer cheaper models for exploration
cost_weight	float	0.2	Cost influence weight
use_router_r1_rewards	bool	false	Enable Router-R1 reward structure
cost_reward_alpha	float	0.3	Performance-cost tradeoff (0=outcome, 1=cost)
enable_llm_routing	bool	false	Enable LLM-as-router mode
router_r1_server_url	string	—	URL of Router-R1 LLM server
llm_routing_fallback	string	thompson	Fallback when LLM routing fails
storage_path	string	—	Persist RL state to file
auto_save_interval	string	30s	Auto-save interval