Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four
  Habits of Highly Effective STaRs
Authors: name: Kanishk Gandhi; name: Ayush Chakravarthy; name: Anikait Singh; name: Nathan Lile; name: Noah D. Goodman
Published: 2025-03-03 08:46:22

Abstract: Test-time inference has emerged as a powerful paradigm for enabling language
models to ``think'' longer and more carefully about complex challenges, much
like skilled human experts. While reinforcement learning (RL) can drive
self-improvement in language models on verifiable tasks, some models exhibit
substantial gains while others quickly plateau. For instance, we find that
Qwen-2.5-3B far exceeds Llama-3.2-3B under identical RL training for the game
of Countdown. This discrepancy raises a critical question: what intrinsic
properties enable effective self-improvement? We introduce a framework to
investigate this question by analyzing four key cognitive behaviors --
verification, backtracking, subgoal setting, and backward chaining -- that both
expert human problem solvers and successful language models employ. Our study
reveals that Qwen naturally exhibits these reasoning behaviors, whereas Llama
initially lacks them. In systematic experimentation with controlled behavioral
datasets, we find that priming Llama with examples containing these reasoning
behaviors enables substantial improvements during RL, matching or exceeding
Qwen's performance. Importantly, the presence of reasoning behaviors, rather
than correctness of answers, proves to be the critical factor -- models primed
with incorrect solutions containing proper reasoning patterns achieve
comparable performance to those trained on correct solutions. Finally,
leveraging continued pretraining with OpenWebMath data, filtered to amplify
reasoning behaviors, enables the Llama model to match Qwen's self-improvement
trajectory. Our findings establish a fundamental relationship between initial
reasoning behaviors and the capacity for improvement, explaining why some
language models effectively utilize additional computation while others
plateau.