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Deep Thinking Strategy

Overview

Deep Thinking allocates computational resources proportionally to problem difficulty, providing harder problems with more reasoning capacity for superior quality.

Key Features

  • Difficulty-Based Scaling: Token allocation scales from min to max based on problem difficulty
  • Iterative Refinement: Multiple iterations with increasing complexity
  • Consistency Tracking: Selects solution with best cross-iteration consistency
  • Adaptive: Automatically calibrates reasoning depth

How It Works

  1. Difficulty Estimation: Analyzes query to estimate problem complexity
  2. Token Allocation: Maps difficulty (0.0-1.0) to token budget
  3. Simple problems: minimal tokens (min_tokens)
  4. Complex problems: maximum tokens (max_tokens)
  5. Iterative Generation: Performs multiple iterations with increasing complexity
  6. Consistency Ranking: Selects answer most consistent across iterations

Configuration

Rust
let config = DeepThinkingConfig {
    min_tokens: 256,      // Minimum for any problem
    max_tokens: 2048,     // Maximum for hardest problems
    num_iterations: 3,    // Number of refinement iterations
};

Token Allocation Formula

Text Only
tokens = min_tokens + (difficulty × (max_tokens - min_tokens))
  • Difficulty 0.0 (simple) → min_tokens tokens
  • Difficulty 0.5 (medium) → avg tokens
  • Difficulty 1.0 (complex) → max_tokens tokens

Advantages

  • Resource Efficiency: Avoids wasting computation on simple problems
  • Quality Improvement: Complex problems receive more thinking capacity
  • Consistency-Based: Selects most robust solutions
  • Transparent: Clear token allocation strategy

Use Cases

  • Variable-difficulty problem solving
  • Adaptive reasoning for mixed query types
  • Resource-constrained environments
  • Ensuring consistency across attempts

Examples

Simple Query

Text Only
"What is 2+2?"
→ Difficulty: 0.1
→ Token Budget: 300 tokens
→ Iterations: 1-2 (quick)

Medium Query

Text Only
"Analyze the time complexity of merge sort"
→ Difficulty: 0.5
→ Token Budget: 1,100 tokens
→ Iterations: 3 (moderate depth)

Complex Query

Text Only
"Design an optimal distributed consensus algorithm"
→ Difficulty: 0.9
→ Token Budget: 1,900 tokens
→ Iterations: 3-5 (deep reasoning)

Performance Tips

  • Set min_tokens based on minimum acceptable answer quality
  • Set max_tokens based on available budget
  • num_iterations = 3-5 works well for most use cases
  • Consistency scoring works best with 3+ iterations

References

  • Deep Thinking Paper: Inference-time Scaling (forthcoming)
  • Related: AutoThink, MCTS