Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
We explore a method for improving the performance of large language models
through self-reflection and reinforcement learning. By incentivizing the model
to generate better self-reflections when it answers incorrectly, we demonstrate
that a model's ability to solve complex, verifiable tasks can be enhanced even
when generating synthetic data is infeasible and only binary feedback is
available. Our framework operates in two stages: first, upon failing a given
task, the model generates a self-reflective commentary analyzing its previous
attempt; second, the model is given another attempt at the task with the
self-reflection in context. If the subsequent attempt succeeds, the tokens
generated during the self-reflection phase are rewarded. Our experimental
results show substantial performance gains across a variety of model
architectures, as high as 34.7% improvement at math equation writing and 18.1%
improvement at function calling. Notably, smaller fine-tuned models (1.5
billion to 7 billion parameters) outperform models in the same family that are
10 times larger. Our novel paradigm is thus an exciting pathway to more useful
and reliable language models that can self-improve on challenging tasks with
limited external feedback.
683ffca568402c738a947f4e
Shelly Bensal
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2025-06-05T07:40:16.151Z
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Shelly Bensal
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shelly-writer
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Umar Jamil
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2025-06-05T07:40:22.194Z
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Umar Jamil
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hkproj
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Christopher Bryant
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Melisa Russak
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2025-06-04T08:53:45.047Z
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Melisa Russak
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melisa
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Kiran Kamble
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2025-06-04T15:03:25.497Z
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Kiran Kamble
user
kiranr
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Dmytro Mozolevskyi
admin_assigned
2025-06-05T07:40:32.265Z
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Dmytro Mozolevskyi
user
dmytro-writer
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Muayad Ali
admin_assigned
2025-06-05T07:40:38.833Z
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Muayad Ali
user
Goldenzero
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Waseem AlShikh
claimed_verified
2025-06-04T15:03:22.388Z
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Waseem AlShikh
user
wassemgtk
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
We explore a method for improving the performance of large language models
through self-reflection and reinforcement learning. By incentivizing the model
to generate better self-reflections when it answers incorrectly, we demonstrate
that a model's ability to solve complex, verifiable tasks can be enhanced even
when generating synthetic data is infeasible and only binary feedback is
available. Our framework operates in two stages: first, upon failing a given
task, the model generates a self-reflective commentary analyzing its previous
attempt; second, the model is given another attempt at the task with the
self-reflection in context. If the subsequent attempt succeeds, the tokens
generated during the self-reflection phase are rewarded. Our experimental
results show substantial performance gains across a variety of model
architectures, as high as 34.7% improvement at math equation writing and 18.1%
improvement at function calling. Notably, smaller fine-tuned models (1.5
billion to 7 billion parameters) outperform models in the same family that are
10 times larger. Our novel paradigm is thus an exciting pathway to more useful
and reliable language models that can self-improve on challenging tasks with
limited external feedback.
A method using self-reflection and reinforcement learning improves the performance of large language models, especially with limited feedback, by rewarding self-reflections that lead to better task performance.
self-reflection reinforcement learning self-reflective commentary performance gains math equation writing function calling parameter-efficient fine-tuning