think-right-reasoning-allocation-calibration

name	think-right-reasoning-allocation-calibration
title	Think Right: Learning Adaptive Reasoning Allocation
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2510.01581
keywords	["reasoning-allocation","difficulty-adaptive","thinking-tokens","efficiency"]
description	Dynamically allocate reasoning budgets per-task: 36.8% length reduction while improving accuracy 8.4% by learning which problems warrant deep reasoning vs. quick answers. Use when optimizing reasoning efficiency across variable-difficulty problems.

Think Right: Learning Adaptive Reasoning Allocation

This work addresses overthinking and underthinking in reasoning models through difficulty-adaptive budget allocation. By learning which problems need extensive reasoning versus quick answers, models achieve both efficiency gains (36.8% length reduction) and accuracy improvements (8.4%).

Core Architecture

• Difficulty estimation: Attention-based features predict task complexity
• Adaptive compression: Prune unnecessary reasoning steps based on estimated difficulty
• Reward structure: Bonus for efficiency on easy problems, bonus for accuracy on hard problems
• Attention-guided selection: Use self-attention patterns to identify important reasoning steps

Implementation Steps

Setup difficulty-adaptive reasoning allocation system:

# Initialize TRAAC (Task-Difficulty-Responsive Adaptive Compression)
from traac import AdaptiveReasoningModel, DifficultyEstimator

difficulty_estimator = DifficultyEstimator(
    features=["token_uncertainty", "attention_entropy", "problem_type"],
    num_difficulty_tiers=3  # easy, medium, hard
)

adapter = AdaptiveReasoningModel(
    base_model=your_reasoning_llm,
    difficulty_estimator=difficulty_estimator,
    compression_strategy="attention_guided"
)

# Configure per-tier reasoning budgets
budget_config = {
    "easy": {"max_thinking_length": 200, "compression_ratio": 0.7},
    "medium": {"max_thinking_length": 500, "compression_ratio": 0.5},
    "hard": {"max_thinking_length": 1000, "compression_ratio": 0.3}
}

Execute RL training with difficulty-aware rewards:

 step, batch  (training_dataloader):
    problems = batch[]
    ground_truth = batch[]

    
    reasoning_full = model.generate_reasoning(
        problem=problems,
        max_length=,
        temperature=
    )

    
    difficulty_scores = difficulty_estimator.estimate(
        reasoning=reasoning_full,
        problem=problems
    )

    difficulty_tiers = torch.argmax(difficulty_scores, dim=)  

    
    compressed_reasoning = []
     i, (reasoning, tier)  ((reasoning_full, difficulty_tiers)):
        budget = budget_config[[, , ][tier]]
        compressed = adapter.compress(
            reasoning=reasoning,
            target_length=((reasoning) * ( - budget[])),
            important_tokens_selector=
        )
        compressed_reasoning.append(compressed)

    
    predictions = model.generate_answer(
        problem=problems,
        reasoning=compressed_reasoning,
        temperature=
    )

    
    rewards = []
     i, (pred, truth, tier)  ((predictions, ground_truth, difficulty_tiers)):
        is_correct = verify_answer(pred, truth)

         tier == :  
            
            reasoning_length = (compressed_reasoning[i])
            efficiency_bonus = (,  - (reasoning_length / ))
            reward = (  is_correct  ) +  * efficiency_bonus
         tier == :  
            
            reward =   is_correct  
        :  
            
            reward =   is_correct  -

        rewards.append(reward)

    rewards = torch.tensor(rewards)

    
    loss = adapter.compute_rl_loss(
        reasoning_lengths=[(r)  r  compressed_reasoning],
        answers=predictions,
        rewards=rewards,
        kl_coefficient=
    )

    loss.backward()
    optimizer.step()
    optimizer.zero_grad()