Title: FEEDBACK FRICTION: LLMs Struggle to Fully Incorporate External Feedback
Venue: NeurIPS 2025
Accepted: yes
Keywords: Language Model

Self-Improvement
Abstract: Recent studies have shown LLMs possess some ability to improve their responses when given external feedback. However, it remains unclear how effectively and thoroughly these models can incorporate extrinsic feedback. In an ideal scenario, if LLMs receive near-perfect and complete feedback, we would expect them to fully integrate the feedback and reach correct solutions. In this paper, we systematically investigate LLMs’ ability to incorporate feedback by designing a controlled experimental environment. For each problem, a solver model attempts a solution, then a feedback generator with access to near-complete ground-truth answers produces targeted feedback, after which the solver tries again. We evaluate this pipeline across a diverse range of tasks, including math reasoning, knowledge reasoning, scientific reasoning, and general multi-domain evaluations with state-of-the-art language models including Claude 3.7 with extended thinking. Surprisingly, even under these near-ideal conditions, solver models consistently show resistance to feedback, a limitation that we term FEEDBACK FRICTION. To mitigate this limitation, we experiment with sampling-based strategies like progressive temperature increases and explicit rejection of previously attempted incorrect answers, which yield improvements but still fail to help models achieve target performance. We analyze FEEDBACK FRICTION and find that models’ confidence on specific questions, measured by semantic entropy, predicts feedback resistance: high-confidence predictions remain resistant to external correction. We hope that highlighting this issue in LLMs will help future research in self-improvement.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper examines the tendency of LLMs to not incorporate external feedback. The paper defines the *Rigid Thinking* effect - showing the models' inability to respond to adequate feedback. The results show the quantitative frequency of *Rigid Thinking* across different tasks for 3 feedback strategies and 3 models. It shows that all models reach saturation significantly below the theoretical limit. Ablation studies also claim that the cause of Rigid thinking does not lie with overconfidence and data familiarity. Finally, this work further investigates promising potential mitigations for the effect of *Rigid Thinking*, showing promise for combatting the effect at test time.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\nThe paper is well explained, with clear experimental setup. Quantifying receptiveness to feedback is an important avenue for research, and proving the majority of the cause is the models' response, rather than the quality of the feedback. The range of the tasks is sensible, including a range of easier, and more challenging ones. Further, the additionally defined tasks are interesting, with the differences in behaviour between hex and decimal computation showing a key limitation in LLMs, not just in the Rigid Thinking sense.\n\nThe observation and quantification of the phenomenon is important, as it highlights the need for future research in this direction.\n\nShowing that this effect can be mitigated is also a key contribution. It would be nice to see if train-time techniques can be used to do this, as the proposed test-time solutions can be computationally expensive\n\nWith the paper's findings, I hope that better train- or test-time mitigations for \"Rigid Thinking\" will be developed as a result.\n## Weaknesses\n\nThe paper is mostly clear and presented well, some decisions for the experimental setup were not thoroughly explained. While the LLMs being evaluated are quite recent, their scope is rather limited. The paper only looks into the most recent LLaMa models, where potentially similar training methods and/or data might have induced similar behaviour. Furthermore, the authors acknowledged that they didn't evaluate on the Qwen3 235B-A22B reasoning model, as it got stuck in reasoning loops. However, in their experimental setting, the authors described using a temperature of 0. This decision is not explained well enough, and is the primary cause for the reasoning loops observed. In particular, basically all open-source reasoning model providers especially warn against using a low temperature. Showing the observations in this work hold across different model providers/reasoning models, would significantly strengthen the paper.\n\nFurthermore, the human-led nature of the study into the factors causing Rigid Thinking can be considerably difficult to scale. The authors show that o4-mini has a near-human performance with a >96\\% agreement rate, yet they do not explain why a human-based evaluation was necessary.\n\nUnfortunately, the paper does not present a clear argument about why the effect exists, as well for eliminating the overconfidence factor. First, the measurement use to quantify confidence is not sufficiently justified. It is further temperature-dependent, given the choice of the sequence of generated tokens is as well. Finally, given the final accuracy is near-saturation, the statistical variability around 100% seems much more significant than the confidence factor. Evaluating on a harder task (i.e. AIME) where accuracy is not almost saturated by the final iteration, might give a stronger signal to support the hypothesis.\n\nA nitpick I had with the feedback preprocessing step is the fact that masking the boxed answer is often insufficient. Especially in more reasoning-heavy tasks, intermediate or near-final steps might leak a significant of information.\n\nOverall, the paper contains some good ideas, but the experimental design leaves key questions and issues not throughly explored.\n\nQUESTIONS:\n1. Large Reasoning Models, such as DeepSeek R1, Qwen3, o4-mini, etc. have been shown to not only be better at reasoning tasks, but also more resistant to external feedback. It would be interesting if the authors could replicate their experiments, with, if necessary, following the pointers I mentioned in the **Strenghts and Weaknesses** section. Given the computational difficulties, and the rather time-consuming nature of human-based evaluations, some results on a smaller model\nsuch as *DeepSeek R1 Distill Qwen3 8B*, *Qwen3 8B*, or similar models with the same properties, would be helpful for their arguments. I aknowledge that not all of these models were not released at the time of the submission deadline but alternatives, such as the DeepSeek R1 Distill 14B were present.\n\n2. In sampling the LLM generations, the authors describe using a deterministic approach (with temperature 0). Why is that necessary for quantifying the phenomenon, and why are different temperature schedules not considered in the main evaluation? Ideally, some experimental evidence would be appreciated.\n\n3. How would using conversation-based feedback (repeated user-assistant iterations), instead of a large prompt containing all the iterations, change the results?\n\n4. Can the authors address the comments about their confidence comparison I made in **Strengths and Weaknesses**?\n\n5. Why did the authors not consider scaling using automated tools? This would allow for much better scaling across multiple models. In most human-based studies, the experts' judgments are not always 100% accurate, so the 96% accuracy rate of o4-mini seems quite high.\n\nIf the authors are able to address these issues, I would be happy to raise my score.\n\nLIMITATIONS:\nThe authors have provided some discussion on the limitations of their work. They acknowledge minor limitations, such as not being able to completely remove the Rigid Thinking phenomenon, as well as not being able to explain why models can behave stubbornly. \n\nHowever, they do not thoroughly discuss the limitations about the scale and replicability of their results for other models and settings. I discuss most of these weaknesses in the **Strengths and Weaknesses** section. In summary:\n- There is no clear reason why no competing open-source models (Gemma/Phi), or reasoning models (o4-mini, Qwen3, R1) were not investigated. Instruction-following behaviour might differ vastly across different model types, or differences in how these models were trained.\n- The authors should discuss the difficulty of evaluating this dataset, and potential for automated scalability in more detail.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper develops a controlled solver <--> feedback-generator loop, showing that even with near-perfect feedback, state-of-the-art Llama-family models plateau well below the theoretical maximum accuracy. This phenomenon is termed rigid thinking. The work explores potential root causes, offers two sampling-based remedies, and identifies feedback resistance rather than poor feedback quality as the primary limitation.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n**Breadth of benchmarks:** The inclusion of nine diverse tasks, such as counter-factual arithmetic, convincingly illustrates that rigid thinking is not limited to a specific domain.\n\n**Thorough error analysis:** The study effectively distinguishes between solver deficiencies and feedback quality issues. The automated categorization, cross-checked by human annotators with a high agreement rate (96%), provides strong evidence supporting the conclusion about feedback resistance.\n\n**Clarity:** The motivation and setting were clear, even for readers less familiar with the topic. Methods and results were presented understandably.\n\n## Weaknesses\n\n**Model selection**: Given that Llama-4 models are not reasoning models, would it be possible to evaluate models specifically designed for reasoning tasks? It would be interesting to see if reasoning-oriented models might incorporate feedback more effectively, particularly on challenging synthetic tasks like Hex, where the accuracy observed is currently very low.\n\n**Temperature setting:** The use of temp=0 for sampling raises concerns, as it generally isn’t recommended and might produce misleading outcomes for long ouputs. Could the authors clarify their rationale for choosing temp=0 despite recommendations from sources like the Qwen3 Huggingface page, which explicitly suggests higher temperature values (e.g., 0.6-0.7)? A comparison using recommended temperatures might strengthen the analysis.\n\n**Prompt structure:** Currently, the entire interaction history is presented in one prompt per model iteration. Wouldn't a multi-turn interaction approach, where history naturally emerges through alternating and prompts, be more realistic and potentially more effective for chat/reasoning models? Could the authors also report accuracy outcomes using such a multi-turn interaction to compare?\n\n**Proprietary reasoning models:** Have the authors considered evaluating proprietary reasoning-focused models such as o4-mini to see if they benefit more significantly from feedback? Even a limited evaluation on a few datasets the authors identify as particularly challenging would provide whether insights generalize to recent frontier models.\n\n**Feedback correctness (Section 4.1, Table 1)**: Specifically for Llama-4-Maverick on GPQA, the feedback resistance (FR) is reported at 85.7%, suggesting approximately 14.3% incorrect feedback. Given that Llama-4-Maverick achieves ~95% accuracy, could the authors clarify how improvement in the remaining ~5% accuracy is expected if around 15% of feedback might be incorrect? Is incorrect feedback primarily an issue for low performance ranges, or does it persist even at high accuracy levels?\n\n**Baseline comparison (Fig. 5):** Could a baseline showing \"no change\" (similar to lines plotted in Figure 3) be included in Figure 5 for clearer comparison?\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nNone", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
This paper investigates whether large language models can fully integrate perfect, targeted feedback to correct their own mistakes through multiple iterations. Despite using strong feedback generators with access to ground truth, the authors find that models consistently plateau below their theoretical maximum accuracy — a phenomenon they call RIGID THINKING. The study systematically analyzes this issue across various reasoning tasks and shows that simple sampling or rejection strategies only partly mitigate the problem.

STRENGTHS AND WEAKNESSES:
Strengths
The paper addresses a crucial and underexplored limitation of LLMs’ self-improvement ability in a rigorous, controlled setting. It combines careful experimental design with diverse tasks and strong baseline models. The concept of RIGID THINKING is clearly defined, well-supported with evidence, and opens an important direction for future work.

Weaknesses
The paper stops short of offering a concrete solution to overcome feedback resistance, beyond simple sampling tricks. It does not deeply connect the observed limitations with potential architectural or training modifications. Some of the tasks (e.g., synthetic multiplication) are very narrow, so broader generalization to more open-ended tasks remains uncertain.

QUESTIONS:
Is there any correlation between model size and resistance to feedback — would even larger models behave differently?

LIMITATIONS:
The study is limited to instruction-tuned LLMs with deterministic decoding setups, which may not reflect real-world use cases involving more diverse sampling. It also doesn’t test feedback loops in more subjective tasks like summarization or dialogue.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

comments: SUMMARY:
This paper investigated LLMs’ ability to incorporate feedback. This is done by designing a controlled experimental environment, where for each problem, the model attempts a solution, then a feedback generator with access to near-complete ground-truth answers produces targeted feedback, after which the model tries again. The paper showed that even under these near-ideal conditions, solver models consistently show resistance to feedback.

STRENGTHS AND WEAKNESSES:
Strengths: 

- The paper discussed the feedback resistance from the model side as a limitation rather than the quality of the feedback.

- The paper shows improvement with increased temperature with rejection sampling.


Weaknesses:

- There has been a lot of existing work that shows the model doesn’t respond to the feedback and they bring up some hypotheses like model size, etc. Here the paper shows consistent results on not leveraging feedback after 2-3 iterations which is interesting but surprising as well. The comparison with existing work analysis is missing.

- More experiments need to be run to get a conclusion here. For example, other LLM models with different sizes. It might also be the fact that how you prompt the model with the feedback.  Sometimes clarifying the feedback with an example is helpful. 

- The paper should compare the results against the baseline where feedback doesn’t come from the  ground-truth answers.

QUESTIONS:
Please look at the weakness section above.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-11