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arxiv: 2606.30774 · v1 · pith:CT6MFEQYnew · submitted 2026-06-29 · 💻 cs.AI

What Drives Interactive Improvement from Feedback?

Bart{\l}omiej Cupia{\l} , Jan {\L}ojek , Miko{\l}aj Garstecki , Szymon Pob{\l}ocki , Alicja Ziarko , Piotr Mi{\l}o\'s This is my paper

Pith reviewed 2026-07-01 02:33 UTC · model grok-4.3

classification 💻 cs.AI
keywords feedbackinteractive agentsself-refinementstudent-teacher protocolmulti-turn improvementnatural language feedbacklanguage modelsevaluation baselines
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The pith

Multi-turn gains from feedback often come from retries rather than the feedback itself.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether natural-language feedback improves language agents beyond what repeated attempts alone produce. In a controlled student-teacher setup across math, coding, and reasoning benchmarks, self-generated feedback performs similarly to unguided self-refinement. Only the strongest external teachers deliver clearly larger gains that can be attributed to the feedback content. Student ability to use the feedback turns out to matter more than which model supplies it. The work concludes that future interactive agents need better mechanisms for acting on feedback, not just more feedback.

Core claim

Across Omni-MATH, Codeforces, BBEH Linguini, and ARC-AGI1 with thirteen open-weight models, multi-turn improvement is often not evidence of feedback use: self-generated feedback adds little beyond unguided self-refinement, whereas the strongest external teachers produce substantially larger feedback-specific gains; dense student-teacher interaction matrices further show that interactive gains are driven more by the student's ability to use feedback than by the teacher's identity, although teacher choice remains important for a fixed student.

What carries the argument

The controlled student-teacher protocol that compares external feedback, self-feedback, and unguided self-refinement while holding constant interaction history, task difficulty, and teacher access to privileged information.

If this is right

  • Feedback-based agents should be evaluated against repeated-attempt baselines.
  • Ability to act on feedback is a central bottleneck for interactive improvement.
  • Self-generated feedback adds little beyond unguided self-refinement.
  • Only the strongest external teachers yield substantially larger feedback-specific gains.
  • For any fixed student, teacher identity still affects outcomes even though student capability dominates overall.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training models specifically to interpret and apply feedback could yield larger gains than improving feedback generation alone.
  • Deployments might benefit more from selecting capable students than from selecting the absolute best teacher.
  • The same patterns could be tested with human-provided feedback to check whether model teachers are unusually limited.
  • Benchmarks that omit unguided retry controls will systematically overestimate the value of feedback mechanisms.

Load-bearing premise

The protocol isolates the causal effect of natural-language feedback from extra test-time computation, format correction, or resampling.

What would settle it

If matching the number of attempts without any feedback produces the same accuracy gains as the strongest external feedback conditions, the claim of feedback-specific improvement would be falsified.

Figures

Figures reproduced from arXiv: 2606.30774 by Alicja Ziarko, Bart{\l}omiej Cupia{\l}, Jan {\L}ojek, Miko{\l}aj Garstecki, Piotr Mi{\l}o\'s, Szymon Pob{\l}ocki.

Figure 1
Figure 1. Figure 1: Multi-turn trajectories separate mostly by student. While stronger teachers like Gemma4- 31B provide noticeable boosts for highly capable students, the dominant vertical separation between the curves is driven by student rather than teacher. Language models (LMs) are increasingly de￾ployed as agents that act, observe, and revise rather than as systems that produce a single isolated answer. Coding agents re… view at source ↗
Figure 2
Figure 2. Figure 2: (Top) Single Didactic Interaction. We transform single-turn problems into multi-turn didactic interactions. A teacher model, conditioned on optionally privileged information (e.g., the ground-truth solution), provides natural language feedback to a student model without revealing the final answer, guiding it to correct its errors. (Bottom) Multi-turn feedback protocol. Each failed student attempt is conver… view at source ↗
Figure 4
Figure 4. Figure 4: Most feedback gains arrive in the first few turns. Each panel reports the marginal improvement from one attempt to the next, averaged over dense-matrix student-teacher pairs. The first feedback-conditioned attempt produces the largest gain, and later turns contribute progressively smaller improvements. This front-loaded pattern suggests that short feedback trajectories capture most of the benefit, while lo… view at source ↗
Figure 5
Figure 5. Figure 5: Student identity explains more gain variation than teacher identity. Points show role means at K = 10, averaging over the opposite role; segments show the min-to-max spread. Across environments, student means span a much wider range than teacher means. 4.1 Feedback-specific gains and interaction budget RF1: Feedback-specific gains are limited unless the teacher provides substantially better guidance than g… view at source ↗
Figure 6
Figure 6. Figure 6: First-turn accuracy and feedback uptake are separate capabilities. Each panel shows one environment, and each point is a student model averaged over teachers. Color denotes model family. The y-axis reports ngain@10 = (acc@10 − acc@1)/(1 − acc@1), the fraction of initially unsolved problems recovered by the interaction. Dashed curves mark equal final acc@10 values. attempts. This cross-product lets us ask w… view at source ↗
Figure 7
Figure 7. Figure 7: History and privileged teacher information help, but for different reasons. Each panel reports auc at K = 10 as we vary the maximum number of previous interaction turns retained in the student and teacher prompts for Gemma4 student-teacher pairs, with line color indicating teacher reference access. RF4: Teacher task competence only partially predicts interactive teacher competence. We use each model’s acc@… view at source ↗
Figure 8
Figure 8. Figure 8: Absolute leak rates across various teacher models and teacher access types, evaluated with [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Omni-MATH dense model matrices. Rows are students and columns are teachers. We annotate all four metrics: acc@1, acc@10, gain, and cumulative accuracy auc. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Codeforces dense model matrices. Rows are students and columns are teachers. We annotate all four metrics: acc@1, acc@10, gain, and cumulative accuracy auc. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: BBEH Linguini dense model matrices. Rows are students and columns are teachers. We annotate all four metrics: acc@1, acc@10, gain, and cumulative accuracy auc. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: ARC-AGI1 dense model matrices. Rows are students and columns are teachers. We annotate all four metrics: acc@1, acc@10, gain, and cumulative accuracy auc. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Cumulative accuracy curves are separated primarily by the student model. Each panel shows one environment, and each curve averages across teachers for one student. The broad vertical separation between students is larger than the within-student variation induced by changing teachers. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Omni-MATH. We decompose gain by student(left) and teacher(right) for each model we [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Codeforces. We decompose gain by student(left) and teacher(right) for each model we [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: BBEH Linguini. We decompose gain by student(left) and teacher(right) for each model [PITH_FULL_IMAGE:figures/full_fig_p026_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: ARC-AGI1. We decompose gain by student (left) and teacher (right) for each model we [PITH_FULL_IMAGE:figures/full_fig_p026_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Gemma4 teacher-information access ablation across environments. Each bar reports cumulative accuracy auc at K = 10 with max history 5, averaged over Gemma4 student-teacher pair cells. Omni-MATH varies final-answer and solution access, Codeforces varies execution-context access, and BBEH Linguini and ARC-AGI1 compare no access against answer access. weaker teacher same model stronger teacher Teacher vs stu… view at source ↗
Figure 19
Figure 19. Figure 19: History lift by relative teacher capability. Matched auc lift from history 1 to history 5 is largest when the teacher is stronger than the student. B.7 Feedback-Control Tables 27 [PITH_FULL_IMAGE:figures/full_fig_p027_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Information-access lift by teacher. Matched information-access gains vary by teacher and environment, rather than appearing as a uniform benefit of revealing the answer or solution. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_20.png] view at source ↗
read the original abstract

We study when natural-language feedback produces improvement beyond the gains obtainable from repeated attempts alone. In multi-turn language agent setting, higher final accuracy can reflect useful feedback, but it can also arise from resampling, format correction, or additional test-time computation. To separate these effects, we introduce a controlled student-teacher protocol across Omni-MATH, Codeforces, BBEH Linguini, and ARC-AGI1, evaluating thirteen open-weight models in both student and teacher roles. We compare external feedback, self-feedback, and unguided self-refinement, while varying interaction history, task difficulty, and teacher access to privileged task information. Across settings, we find that multi-turn improvement is often not evidence of feedback use: self-generated feedback adds little beyond unguided self-refinement, whereas the strongest external teachers produce substantially larger feedback-specific gains, suggesting that useful feedback must provide guidance beyond generic retry. Dense student-teacher interaction matrices further show that interactive gains are driven more by the student's ability to use feedback than by the teacher's identity, although teacher choice remains important for a fixed student. These results suggest that feedback-based agents should be evaluated against repeated-attempt baselines, and that ability to act on feedback, not merely feedback availability, is a central bottleneck for interactive improvement. We release our controlled student-teacher evaluation framework at https://j-lojek.github.io/feedback-generation-is-a-bottleneck/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript claims that multi-turn improvement in language agents from natural-language feedback is often not evidence of actual feedback utilization. Using a controlled student-teacher protocol on Omni-MATH, Codeforces, BBEH Linguini, and ARC-AGI1 with thirteen open-weight models in student and teacher roles, the authors compare external feedback, self-feedback, and unguided self-refinement while varying interaction history, task difficulty, and teacher access to privileged information. They find self-generated feedback adds little beyond unguided self-refinement, while the strongest external teachers produce substantially larger feedback-specific gains; interactive gains are driven more by the student's ability to use feedback than by teacher identity. The work recommends evaluating feedback-based agents against repeated-attempt baselines and releases the evaluation framework.

Significance. If the central empirical findings hold after confirming the protocol controls, the paper makes a useful contribution by providing a large-scale, multi-benchmark comparison that challenges the interpretation of multi-turn gains as feedback-driven. The release of the controlled evaluation framework is a clear strength that supports reproducibility. The distinction between self-refinement and external feedback effects, and the emphasis on student utilization ability as the bottleneck, offers actionable guidance for designing and evaluating interactive agents.

major comments (2)
  1. [Abstract] Abstract (protocol description): the central claim that higher accuracy reflects feedback-specific gains rather than repeated-attempt or compute effects requires that the unguided self-refinement baseline exactly matches feedback conditions in total model calls, context length, and any format-correction steps. The provided description states the protocol separates these effects but does not detail the equalization procedure; this is load-bearing because unequal compute would inflate apparent feedback-specific gains from strong teachers and undermine the conclusion that self-feedback adds little beyond unguided refinement.
  2. [Results] Results on dense student-teacher interaction matrices: the claim that gains are driven more by student ability than teacher identity is presented as a key finding, but without reported variance decomposition, statistical tests for relative effect sizes, or controls for student-teacher pairing imbalances, it is difficult to assess whether this holds after accounting for the varying teacher strengths already shown in the external vs. self conditions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The two major comments identify areas where additional clarity would strengthen the manuscript. We address each below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract] Abstract (protocol description): the central claim that higher accuracy reflects feedback-specific gains rather than repeated-attempt or compute effects requires that the unguided self-refinement baseline exactly matches feedback conditions in total model calls, context length, and any format-correction steps. The provided description states the protocol separates these effects but does not detail the equalization procedure; this is load-bearing because unequal compute would inflate apparent feedback-specific gains from strong teachers and undermine the conclusion that self-feedback adds little beyond unguided refinement.

    Authors: We agree that explicit equalization details are necessary for the central claim. The protocol matches total model calls by giving the unguided baseline the same number of generation attempts as the feedback conditions (one initial attempt plus the same number of refinement turns). Context length is equalized by inserting neutral placeholder messages of comparable token length in the unguided condition. Format-correction steps are applied identically across all conditions. We will add a dedicated subsection in the Methods describing these controls with pseudocode and token-count verification. revision: yes

  2. Referee: [Results] Results on dense student-teacher interaction matrices: the claim that gains are driven more by student ability than teacher identity is presented as a key finding, but without reported variance decomposition, statistical tests for relative effect sizes, or controls for student-teacher pairing imbalances, it is difficult to assess whether this holds after accounting for the varying teacher strengths already shown in the external vs. self conditions.

    Authors: The dense matrices are intended to convey the pattern through direct visual comparison across all 13×13 pairs rather than aggregate statistics. Student rows exhibit substantially larger variance in feedback-specific gains than teacher columns, and strong students paired with weak teachers still outperform weak students paired with strong teachers. While we did not compute formal variance decomposition or paired statistical tests, the raw per-pair results already control for pairing imbalances by enumerating every combination. We will add a supplementary table reporting mean gain differences and standard deviations across student vs. teacher marginals to make the relative effect sizes more quantitative. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical protocol with no derivations or fitted predictions

full rationale

The paper presents an empirical comparison across multiple benchmarks and models, measuring accuracy deltas under controlled conditions (external feedback, self-feedback, unguided self-refinement). No mathematical derivations, first-principles predictions, fitted parameters renamed as outputs, or self-citation chains are used to generate the reported results. The central claims follow directly from the experimental measurements rather than reducing to inputs by construction. The protocol's isolation of feedback effects is an empirical assumption open to falsification by the data, not a definitional or self-referential step.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard empirical assumptions in machine learning evaluation (benchmarks measure relevant capability, statistical comparisons are valid) rather than new free parameters or invented entities. No fitted constants or new postulated objects are described in the abstract.

axioms (2)
  • domain assumption The chosen benchmarks (Omni-MATH, Codeforces, BBEH Linguini, ARC-AGI1) provide valid measures of the capabilities under study
    Invoked when generalizing results from these tasks to interactive agent improvement in general
  • domain assumption Differences in final accuracy after feedback can be attributed to feedback use rather than unmeasured factors
    Central to interpreting the student-teacher matrices as evidence about feedback utilization

pith-pipeline@v0.9.1-grok · 5810 in / 1425 out tokens · 43315 ms · 2026-07-01T02:33:11.391198+00:00 · methodology

discussion (0)

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Reference graph

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