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REVIEW 2 major objections 2 minor 27 references

Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks

T0 review · 2 major / 2 minor · reviewed 2026-05-24 · grok-4.3

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Pith's one-line read No single feedback frequency is optimal in interactive RL for robots; it should adapt as the agent improves.

desk verdict The paper runs fixed-frequency comparisons across manipulator arms of different complexity and finds that best frequency depends on task difficulty, but the claim that frequency should adapt with proficiency is an untested inference from those cross-arm results. read the letter →

arxiv 2207.09845 v2 submitted 2022-07-20 cs.RO cs.AIcs.HCcs.LG

Daniel Harnack , Julie Pivin-Bachler , Nicolás Navarro-Guerrero This is my paper
classification cs.ROcs.AIcs.HCcs.LG
keywords interactivereinforcementlearningfeedbackfrequencyroboticmanipulatorsinversekinematicscontinuouscontrolhuman-in-the-loopdataefficiency
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper isolates how often human feedback is given during reinforcement learning for robotic arms performing inverse kinematics. Experiments across arms of varying complexity reveal that reported contradictions in prior work arise because the best frequency changes with task difficulty and with the agent's growing skill. A sympathetic reader would care because interactive feedback is one practical route to making RL data-efficient enough for real robots, yet current strategies lack agreement on timing. The central result is that fixed frequencies are insufficient; frequency needs to decrease as proficiency rises.

What carries the argument

Feedback frequency as the variable isolated in interactive reinforcement learning loops for continuous robotic control.

What would settle it

An experiment in which the same fixed feedback frequency produces equivalent learning curves across multiple arm complexities would falsify the claim that frequency effects are complexity-dependent.

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Extended reading notes

Core claim

Experiments on inverse kinematics for manipulator arms of different complexity demonstrate that seemingly contradictory phenomena in the literature appear at distinct complexity levels. No single ideal feedback frequency exists. Feedback frequency should instead be changed as the agent's proficiency in the task increases.

Load-bearing premise

Differences observed across arm complexities are caused by feedback frequency rather than other details of the reinforcement learning setup.

Editorial extensions

If this is right

  • Reported optimal frequencies in earlier studies are artifacts of the particular arm complexity tested.
  • Feedback should be provided more often early in training and less often once the agent improves.
  • Strategies must be re-evaluated when moving from simple to complex continuous control tasks.
  • Interactive RL can be made more efficient by scheduling frequency changes rather than using a constant rate.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An adaptive schedule that monitors agent performance to adjust feedback rate could outperform any fixed schedule.
  • The same frequency-adaptation principle might apply to other continuous robotic tasks such as grasping or locomotion.
  • Real-robot tests with noisy human feedback would be required to check whether the simulated trends survive sensor and actuator imperfections.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript reports experiments isolating the effect of feedback frequency in interactive reinforcement learning for inverse kinematics tasks on robotic manipulator arms of increasing complexity. It finds that different phenomena appear at different complexity levels and concludes that feedback frequency should be adapted as the agent's proficiency increases rather than using a fixed frequency.

Significance. If the results hold after addressing the extrapolation issue, the work would contribute empirical guidance on feedback scheduling in interactive RL for continuous robotic control tasks, a step beyond discrete environments. The experimental focus on arms of varying complexity is a strength for generalizability claims.

major comments (2)
  1. [Abstract and §5] Abstract and §5 (Results/Discussion): The prescriptive claim that 'feedback frequency should be changed as the agent's proficiency in the task increases' extrapolates from comparisons of fixed frequencies across arms of different complexity; the manuscript does not report within-run adaptation experiments or ablations testing dynamic adjustment against the best fixed schedule on a single arm.
  2. [§4] §4 (Experimental Setup): The abstract and methods description provide no details on controls, sample sizes per condition, statistical tests, or potential confounds (e.g., hyperparameter differences across arm complexities), which are load-bearing for assessing whether observed differences are attributable to feedback frequency.
minor comments (2)
  1. Clarify notation for feedback frequency (e.g., episodes vs. timesteps) and ensure all figures include error bars or statistical annotations.
  2. [Related Work] Add explicit comparison to prior interactive RL work on feedback timing to better situate the contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, agreeing where revisions are warranted and clarifying the basis for our claims.

read point-by-point responses

  1. Referee: [Abstract and §5] Abstract and §5 (Results/Discussion): The prescriptive claim that 'feedback frequency should be changed as the agent's proficiency in the task increases' extrapolates from comparisons of fixed frequencies across arms of different complexity; the manuscript does not report within-run adaptation experiments or ablations testing dynamic adjustment against the best fixed schedule on a single arm.

    Authors: We agree that the suggestion for adapting feedback frequency is inferred from the observed shifts in optimal fixed frequencies across arms of increasing complexity (which serve as proxies for rising task difficulty and proficiency). The manuscript does not include explicit within-run dynamic adaptation experiments or direct ablations comparing dynamic schedules to fixed ones on the same arm. We will revise the abstract and §5 to frame this as an empirically motivated hypothesis and direction for future work rather than a directly validated prescriptive recommendation. revision: yes

  2. Referee: [§4] §4 (Experimental Setup): The abstract and methods description provide no details on controls, sample sizes per condition, statistical tests, or potential confounds (e.g., hyperparameter differences across arm complexities), which are load-bearing for assessing whether observed differences are attributable to feedback frequency.

    Authors: The referee is correct that §4 lacks explicit reporting of these details. We will expand the experimental setup section to specify the number of independent runs per condition, the statistical tests applied, controls for confounds, and confirmation that core hyperparameters were held constant across arm complexities (with only task-specific adjustments as needed). revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Purely experimental quantification; no derivation chain present

full rationale

The paper reports empirical results from fixed-frequency experiments on inverse kinematics tasks across manipulator arms of varying complexity. No equations, fitted parameters, self-citations, or ansatzes are invoked to derive the central claims; the suggestion that frequency should vary with proficiency is an interpretive inference from the observed patterns rather than a reduction of any claimed prediction to its inputs. No load-bearing steps match the enumerated circularity patterns.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The paper relies on standard assumptions in RL and robotics simulation for isolating frequency effects; no free parameters, new entities, or ad-hoc axioms are mentioned in the abstract.

assumptions (1)
  • domain assumption Interactive feedback can be provided at varying frequencies in RL without altering other aspects of the learning process
    Invoked when isolating the effect of frequency across different arm complexities

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Cite this review

Pith. "Pith review of Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks." pith.science (2026). https://pith.science/paper/2207.09845

@misc{pith2026220709845,
  author       = {Pith},
  title        = {Pith review of: Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2207.09845}},
  note         = {Machine review of arXiv:2207.09845}
}
read the original abstract

Reinforcement learning (RL) has become widely adopted in robot control. Despite many successes, one major persisting problem can be very low data efficiency. One solution is interactive feedback, which has been shown to speed up RL considerably. As a result, there is an abundance of different strategies, which are, however, primarily tested on discrete grid-world and small scale optimal control scenarios. In the literature, there is no consensus about which feedback frequency is optimal or at which time the feedback is most beneficial. To resolve these discrepancies we isolate and quantify the effect of feedback frequency in robotic tasks with continuous state and action spaces. The experiments encompass inverse kinematics learning for robotic manipulator arms of different complexity. We show that seemingly contradictory reported phenomena occur at different complexity levels. Furthermore, our results suggest that no single ideal feedback frequency exists. Rather that feedback frequency should be changed as the agent's proficiency in the task increases.

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