In-Place Feedback: A New Paradigm for Guiding LLMs in Multi-Turn
  Reasoning
Large language models (LLMs) are increasingly studied in the context of
multi-turn reasoning, where models iteratively refine their outputs based on
user-provided feedback. Such settings are crucial for tasks that require
complex reasoning, yet existing feedback paradigms often rely on issuing new
messages. LLMs struggle to integrate these reliably, leading to inconsistent
improvements. In this work, we introduce in-place feedback, a novel interaction
paradigm in which users directly edit an LLM's previous response, and the model
conditions on this modified response to generate its revision. Empirical
evaluations on diverse reasoning-intensive benchmarks reveal that in-place
feedback achieves better performance than conventional multi-turn feedback
while using 79.1% fewer tokens. Complementary analyses on controlled
environments further demonstrate that in-place feedback resolves a core
limitation of multi-turn feedback: models often fail to apply feedback
precisely to erroneous parts of the response, leaving errors uncorrected and
sometimes introducing new mistakes into previously correct content. These
findings suggest that in-place feedback offers a more natural and effective
mechanism for guiding LLMs in reasoning-intensive tasks.
68dddb2c6024653e8a3ed07a
Youngbin Choi
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2025-10-22T15:47:02.489Z
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/avatars/fb2c47dc558653e7f3ed2c959fe5d2d8.svg
choi
user
Youngbin1
68dddb2c6024653e8a3ed07b
Minjong Lee
68dddb2c6024653e8a3ed07c
Saemi Moon
68dddb2c6024653e8a3ed07d
Seunghyuk Cho
68dddb2c6024653e8a3ed07e
Chaehyeon Chung
68dddb2c6024653e8a3ed07f
MoonJeong Park
68dddb2c6024653e8a3ed080
Dongwoo Kim
In-Place Feedback: A New Paradigm for Guiding LLMs in Multi-Turn
  Reasoning
Large language models (LLMs) are increasingly studied in the context of
multi-turn reasoning, where models iteratively refine their outputs based on
user-provided feedback. Such settings are crucial for tasks that require
complex reasoning, yet existing feedback paradigms often rely on issuing new
messages. LLMs struggle to integrate these reliably, leading to inconsistent
improvements. In this work, we introduce in-place feedback, a novel interaction
paradigm in which users directly edit an LLM's previous response, and the model
conditions on this modified response to generate its revision. Empirical
evaluations on diverse reasoning-intensive benchmarks reveal that in-place
feedback achieves better performance than conventional multi-turn feedback
while using 79.1% fewer tokens. Complementary analyses on controlled
environments further demonstrate that in-place feedback resolves a core
limitation of multi-turn feedback: models often fail to apply feedback
precisely to erroneous parts of the response, leaving errors uncorrected and
sometimes introducing new mistakes into previously correct content. These
findings suggest that in-place feedback offers a more natural and effective
mechanism for guiding LLMs in reasoning-intensive tasks.
In-place feedback allows users to directly edit LLM responses, improving performance and reducing token usage in multi-turn reasoning tasks.
large language models multi-turn reasoning in-place feedback feedback paradigms token usage reasoning-intensive benchmarks controlled environments