Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four
  Habits of Highly Effective STaRs
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.
67c68adc0457c9f809c22df8
Kanishk Gandhi
claimed_verified
2025-03-04T08:35:01.161Z
63e6a880f2e9a8f22c5a1630
/avatars/53b57690fe052ce6882bbfc87b11567c.svg
Kanishk Gandhi
user
obiwan96
67c68adc0457c9f809c22df9
Ayush Chakravarthy
admin_assigned
2025-03-04T10:04:44.344Z
624f9e3d07bd004fb855f5e9
/avatars/86a349cd4053bc0317e27e75a51c69fa.svg
Ayush Chakravarthy
user
ayushchakravarthy
67c68adc0457c9f809c22dfa
Anikait Singh
admin_assigned
2025-03-04T10:05:05.759Z
6511ee845b7e52b0251fdee9
https://cdn-avatars.huggingface.co/v1/production/uploads/6511ee845b7e52b0251fdee9/hTIwiIYBGOVnIrxtpri83.png
Anikait Singh
user
Asap7772
67c68adc0457c9f809c22dfb
Nathan Lile
claimed_verified
2025-03-04T08:34:58.582Z
61aa15fd8a9625ebfe284286
https://cdn-avatars.huggingface.co/v1/production/uploads/61aa15fd8a9625ebfe284286/KaGzIeijcgcN15JErCqft.jpeg
nathan lile
user
nlile
67c68adc0457c9f809c22dfc
Noah D. Goodman
admin_assigned
2025-03-04T10:05:12.186Z
67321274c1f20c742bcf7a8d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ltcQhre6eDRVzn6Vbbyhu.png
Noah D. Goodman
user
ngoodman
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four
  Habits of Highly Effective STaRs
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.
Research reveals that effective self-improvement in language models is linked to their ability to perform specific reasoning behaviors, such as verification and backward chaining, which can be primed through appropriate training.
test-time inference reinforcement learning Qwen-2.5-3B Llama-3.2-3B reasoning behaviors verification backtracking subgoal setting backward chaining priming OpenWebMath