Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models

• URL: http://arxiv.org/abs/2608.13760v1
• Date: Thu, 13 Aug 2026 20:37:59 GMT
• Title: Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models
• Abstract summary: reasoning-oriented training can make traces look more deliberative without amplifying the behaviors most tied to model correctness.<n>We quantify this mismatch with Behavioral Lift, a metric that measures how much correctness changes when a behavior is present versus absent in a model's reasoning trace.<n>We find evidence for an Amplification-Lift Gap, in which thinking models strongly amplify self-awareness, hypothesis testing, and uncertainty acknowledgment.
• Score: 56.88565331713778
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Which reasoning behaviors are associated with correct answers in reasoning models, and does reasoning-oriented training amplify those behaviors? This distinction is important because reasoning-oriented training can make traces look more deliberative without amplifying the behaviors most tied to model correctness. We quantify this mismatch with Behavioral Lift, a metric that measures how much correctness changes when a behavior is present versus absent in a model's reasoning trace. Across 15 models and 6 benchmarks spanning text-only and vision-language reasoning, we annotate 15,282 traces with a taxonomy whose core behaviors are defined for both LLM and VLM traces. We find evidence for an Amplification-Lift Gap, in which thinking models strongly amplify self-correction, hypothesis testing, and uncertainty acknowledgment, while the highest-lift behaviors are confidence calibration, knowledge alignment, and self-awareness. Confidence calibration is among the strongest positive signals of correctness in both modalities, yet is barely amplified; uncertainty acknowledgment is amplified by 3--7$\times$, yet is weakly or negatively associated with correctness. We find that reasoning-oriented training does not preferentially amplify the highest-Lift behaviors, motivating process-level objectives that reward calibrated and grounded reasoning rather than surface form alone.

Related papers

• Does Reasoning Preserve Alignment? On the Trustworthiness of Large Reasoning Models [55.788110316999166]
  We compare reasoning models produced via supervised fine-tuning, RL-based post-training, and distillation against instruction-tuned baselines.<n>We observe that reasoning models often improve on reasoning benchmarks but exhibit alignment regressions.<n>These regressions are consistent with behavioral drift from the instruction-tuned baseline, measured by KL divergence.
  arXiv  Detail & Related papers  (2026-06-09T16:14:27Z)
• Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs [31.34530174385469]
  Latent Reward Steering (LRS) is an adaptive inference-time framework that promotes cognitive behaviors.<n>LRS trains a latent reward model on reasoning traces by final answer correctness to estimate the quality of intermediate latent states.<n> Experiments on multiple reasoning backbones and benchmarks show that ours consistently improves performance over various baselines.
  arXiv  Detail & Related papers  (2026-05-30T13:38:06Z)
• Understanding and Mitigating Premature Confidence for Better LLM Reasoning [76.16007941549857]
  Long chains of thought (CoT) from current language models frequently contain logical gaps and unjustified leaps.<n>We find such a signal in how the model's confidence evolves during reasoning.<n>We exploit this in progressive confidence shaping, a reinforcement learning objective that trains models to update their confidence as they reason rather than commit early.
  arXiv  Detail & Related papers  (2026-05-23T04:42:45Z)
• Thinking, Faithful and Stable: Mitigating Hallucinations in LLMs [0.4115305983711515]
  This project develops a self correcting framework for large language models (LLMs)<n>Rather than relying solely on final answer correctness, our approach leverages fine grained uncertainty signals.<n>We design a composite reward function that penalizes unjustified high confidence and entropy spikes.
  arXiv  Detail & Related papers  (2025-11-19T23:09:26Z)
• Thinking Out Loud: Do Reasoning Models Know When They're Right? [25.683190106676893]
  Large reasoning models (LRMs) have recently demonstrated impressive capabilities in complex reasoning tasks.<n>We investigate how LRMs interact with other model behaviors by analyzing verbalized confidence.<n>We find that reasoning models may possess a diminished awareness of their own knowledge boundaries.
  arXiv  Detail & Related papers  (2025-04-09T03:58:19Z)
• SEAL: Steerable Reasoning Calibration of Large Language Models for Free [58.931194824519935]
  Large Language Models (LLMs) have demonstrated compelling capabilities for complex reasoning tasks via the extended chain-of-thought (CoT) reasoning mechanism.<n>Recent studies reveal substantial redundancy in the CoT reasoning traces, which negatively impacts model performance.<n>We introduce SEAL, a training-free approach that seamlessly calibrates the CoT process, improving accuracy while demonstrating significant efficiency gains.
  arXiv  Detail & Related papers  (2025-04-07T02:42:07Z)
• Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs [28.565225092457897]
  Reinforcement learning can drive self-improvement in language models on verifiable tasks.<n>We find that Qwen-2.5-3B far exceeds Llama-3.2-3B under identical RL training for the game of Countdown.<n>Our study reveals that Qwen naturally exhibits these reasoning behaviors, whereas Llama initially lacks them.
  arXiv  Detail & Related papers  (2025-03-03T08:46:22Z)
• Selective Learning: Towards Robust Calibration with Dynamic Regularization [79.92633587914659]
  Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. We introduce Dynamic Regularization (DReg) which aims to learn what should be learned during training thereby circumventing the confidence adjusting trade-off.
  arXiv  Detail & Related papers  (2024-02-13T11:25:20Z)
• Improving the Reliability of Large Language Models by Leveraging Uncertainty-Aware In-Context Learning [76.98542249776257]
  Large-scale language models often face the challenge of "hallucination" We introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output in response to uncertainty.
  arXiv  Detail & Related papers  (2023-10-07T12:06:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.