MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference

Giorgia Ramponi; Maria Gkoulta; Mennatallah El-Assady; Rapha\"el Baur; Thomas Kleine Buening; Yannick Metz

REVIEW 4 major objections 5 minor 19 references

MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference

T0 review · 4 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read MAVRL claims that learning a reward function from demonstrations, comparisons, ratings, and stops as a single Bayesian posterior is feasible, and that the combined posterior beats any single feedback type in policy return, reward recovery,

desk verdict A credible, clean extension of AVRIL to multiple feedback types, but the empirical center is an in-sample self-consistency check: all synthetic feedback is generated from the same likelihood models the method learns with. read the letter →

arxiv 2602.15206 v2 pith:4IND3MJH submitted 2026-02-16 cs.LG cs.AI

Raphaël Baur , Yannick Metz , Maria Gkoulta , Mennatallah El-Assady , Giorgia Ramponi , Thomas Kleine Buening This is my paper · ORCID

classification cs.LGcs.AI

keywords rewardlearningmulti-typefeedbackamortizedvariationalinferenceBayesianinversereinforcementpairwisecomparisonsordinalregressionhazardmodelrobustness

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

This paper tries to establish that reward functions for reinforcement-learning agents can be inferred jointly from several qualitatively different kinds of human feedback—demonstrations, pairwise comparisons, ratings, and emergency stops—by treating each feedback type as a probabilistic observation of one shared latent reward, rather than training separate models and merging them. The proposed method, MAVRL, learns a single variational reward encoder plus feedback-specific likelihood decoders and optimizes one evidence lower bound, so no manual loss balancing is needed. On grid-world and continuous-control benchmarks, jointly inferred reward posteriors are reported to outperform single-type baselines in downstream policy return and reward recovery, and to give policies that degrade more gracefully under environment perturbations. A sympathetic reader would care because human feedback in practice is heterogeneous, and current practice either uses one modality or combines modalities with heuristics; if this Bayesian formulation works, it provides a principled way to pool all available feedback and to read model uncertainty as a signal of which parts of the reward are well constrained.

What carries the argument

The central object is a shared variational reward encoder qθ(R|s,a,s′) modeled as a Gaussian whose mean and variance are neural-network functions of state, action, and next state; sampling from it induces distributions over trajectory returns. Alongside it, a Q-value network supplies action values, and a temporal-difference regularization term enforces that encoder rewards match one-step Bellman differences. Each feedback type contributes an explicit likelihood decoder—softmax-rational demonstrations, logistic pairwise comparisons, ordered-logit ratings, and a discounted-regret hazard model for stops—so the whole system is trained by maximizing one evidence lower bound with a KL regularizer.

What would settle it

Run MAVRL on feedback whose generative model is deliberately mismatched to the assumed likelihoods—for example, ratings generated by context-dependent thresholds or stops triggered by a fixed safety rule rather than discounted regret—and compare reward recovery and policy return against single-type baselines; the central claim weakens if the matched-model synthetic gains disappear under mismatched generators. A complementary check is to collect a modest amount of real human feedback in one of the grid environments and test whether combined feedback still outperforms the best single type.

Watch

Extended reading notes

Core claim

MAVRL claims that the shared-latent Bayesian formulation is both tractable and practically effective: by optimizing a single evidence lower bound whose per-modality likelihood terms are a softmax-rational demonstration model, a logistic pairwise-comparison model, an ordered-logit rating model, and a cumulative-regret hazard model for stops, a single reward posterior can exploit complementary information across feedback types. Concretely, the paper reports that combining all four types yields strong normalized returns in five of six environments, best or second-best reward-recovery distance (a shaping-invariant measure) in two of three tabular environments, and more robust performance under d

Load-bearing premise

The central assumption is that human feedback of each type is generated by the specific probabilistic models the paper uses as likelihoods (softmax-rational demonstrations, logistic pairwise comparisons, ordered-logit ratings, and discounted-regret hazard stops); if real humans deviate from these models, the claimed complementarity and robustness benefits may not transfer.

Editorial extensions

If this is right

Practitioners can add a new feedback type by defining one likelihood decoder, without retraining the encoder or manually reweighting losses.
Combined-feedback reward models should produce policies that keep more return under dynamics perturbations than policies trained on any single feedback type, as reported across the three perturbation scenarios.
The reward posterior's variance offers an interpretable map of where the reward is constrained—demonstrations pin down trajectories, ratings locate goals, stops mark unsafe regions, comparisons fill the middle ground—usable for inspecting confidence and consistency.
In sparse-reward settings, added modalities can sometimes add noise: the paper reports a LunarLander-v3 case where standalone demonstrations outperform the full combination, so combining all available feedback is not always optimal.
Because the objective factorizes over feedback types, training can proceed asynchronously and in any order, with no staged curriculum.

Reading between the lines

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

Editorial inference: because the likelihoods are explicit, the same posterior confidence map could drive active data collection; a direct extension would query whichever feedback type is expected to reduce reward uncertainty most.
Editorial inference: if a feedback type in real deployment consistently lowers the evidence lower bound, that is a diagnostic signal that its assumed likelihood is misspecified, which the paper does not explore.
Editorial inference: the observed per-modality uncertainty signatures suggest a practical collection heuristic—use stops and ratings to anchor reward extremes, comparisons to cover the state space, and demonstrations to pin down trajectories—though the paper does not test this protocol.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: MAVRL is a legitimate extension of AVRIL that swaps the single demonstration likelihood for a set of feedback-specific likelihoods inside one variational objective. The rating and stop likelihoods are the genuinely new pieces. But read the experiments with caution: every piece of synthetic feedback is generated from exactly the likelihood models the method trains with (Appendix A.2), so the complementarity and robustness results are in-sample self-consistency, not evidence about real human feedback. The paper admits this in Section 7, yet the abstract and conclusion still state the transfer claim as established.

What is well done: The formulation is clean. A shared reward encoder plus a collection of likelihood decoders, trained by maximizing a single ELBO, is a natural and sensible unification, and the TD-regularization from AVRIL carries over smoothly. The rating likelihood uses ordered-logit cutpoints, which is appropriate for Likert-scale data, and the stop likelihood as a discrete-time hazard over discounted regret is a reasonable first model for a genuinely under-studied feedback channel. The experimental sweep is broad: multiple grid worlds and continuous-control tasks, single-type ablations, EPIC distances, robustness plots, standard errors, and the code is supplied. That is solid, reproducible work by the standards of this area.

The soft spots are in the empirical claims, not the algorithm. First, circularity: because the simulator uses the same Bradley-Terry, ordered-logit, Boltzmann-rational, and hazard models, Tables 1 and 2 mostly show that MAVRL can invert its own generative process. If human ratings are not ordinal-logistic or stops follow a fixed threshold, the gains may shrink or become negative. That gap is addressable — run a mismatched-generator sensitivity study or a small human study — but as it stands the paper's central conclusion is under-supported. Second, there is no comparison to any existing multi-type integration method, including the additive loss-weighting approach of Mehta and Losey (2024), which is cited and closest. Without that baseline, we cannot tell whether joint Bayesian inference actually beats heuristic fusion. Third, the claim of no manual loss balancing is diluted by the fact that lambda_KL and lambda_TD are tuned per environment on the evaluation metric; this is common, but should be acknowledged. Fourth, several combinations underperform their single-type constituents (e.g., CartPole), so "complementary strengths" is a tendency, not a guarantee.

Verdict: worth a serious referee. The algorithmic contribution is real and the paper is honestly written. I'd send it to review with the request that the authors add a mismatched-model sensitivity check and at least one heuristic-fusion baseline before publication. For a reading group on reward learning, yes, bring it.

Referee Report

4 major / 5 minor

Summary. The paper proposes MAVRL, a method that frames reward learning from multiple feedback types (preferences, demonstrations, ratings, and stops) as Bayesian inference over a shared latent reward, combining feedback-specific likelihood models into a single amortized variational ELBO. The authors claim that joint inference exploits complementary information across feedback types, improving reward recovery, downstream policy performance, and robustness to environment perturbations. Experiments on grid-world and continuous-control benchmarks compare single-type and multi-type feedback configurations, with all feedback generated synthetically from the same likelihood models used by the method.

Significance. The methodological contribution is well-motivated and principled: replacing heuristic loss weighting with a unified ELBO is a natural extension of AVRIL, and the framework is extensible to new feedback types. The paper provides code and thorough ablation-style tables across multiple environments. However, the empirical validation is entirely internal to the model's own generative assumptions, and the reported results are not compared against existing multi-type integration approaches. If the claims were supported by external feedback or misspecification-robust experiments, the framework would be a valuable contribution to reward learning; in its current form, the central claims outrun the evidence.

major comments (4)

[Section 6, Appendix A.2] The evaluation is circular with respect to the likelihood models. Preferences, demonstrations, ratings, and stops are simulated using exactly the likelihood models defined in Section 4 (Bradley-Terry, Boltzmann-rational, quantile-cutpoint ordered logit, cumulative-regret hazard). Thus Tables 1-3 and Figure 3 show that MAVRL can invert its own generative process, not that it combines realistic human feedback. The conclusion in Section 7 that joint inference 'improves reward recovery, downstream policy performance, and robustness' is not supported by this in-sample validation. Please add experiments with human feedback or with misspecified generators (e.g., threshold-based stops, non-logistic ratings, label noise) to show the benefits survive model mismatch.
[Appendix A.1] The hyperparameters λ_KL and λ_TD are selected per environment from {0.5, 1.0} using 'the same tuning procedure applied uniformly to all methods.' If this selection is based on the evaluation metric (normalized return or EPIC distance) on the test environments, the reported numbers are optimistically biased. The protocol does not specify a validation split. This is load-bearing because all comparisons in Tables 1-2 and Figure 3 depend on these hyperparameters. Please state whether a validation set was used; if not, report results with fixed default hyperparameters.
[Section 6.2, Table 1] The claim that PDR 'achiev[es] either the best performance or results within 1% of the best performance ... in five out of six environments, with the exception of CartPole-v1' is contradicted by the table. In grid_sparse, PDR (70.0) is 6.7% below the best (75.0); in LunarLander-v3, PDR (80.4) is far below demonstrations alone (115.6); in CartPole-v1 PDR (87.5) is below the best (97.7). Thus the 'strongest overall performance' conclusion is overstated. The analysis should acknowledge these exceptions and discuss when combining feedback types helps rather than hurts.
[Section 6] No comparison to existing multi-type feedback integration methods is provided. Baselines are single-type instances of MAVRL (e.g., demonstrations only, ratings only), but the paper's contribution is about combining feedback types. Without a baseline such as additive loss weighting (Mehta and Losey 2024), post-hoc ensemble of single-type models (Metz et al. 2025), or the Bıyık et al. (2022) demonstration+preference method, the experiments do not establish that joint Bayesian inference is superior to heuristic integration. Please add at least one existing multi-type method as a comparison.

minor comments (5)

[Equation (3)] In the stop likelihood, the hazard function is defined as h_{λ,ρ}^R(ξ, τ), but the product in Eq. (3) uses h_λ^R(ξ, t) with ρ omitted. Align the notation.
[Figure 2] Figure 2 says '2 demonstrations', while Section 6 states nd=1 for grid worlds. Clarify the budget used in the figure.
[Section 5.2] The claim of 'no manual loss balancing' is misleading because Eq. (4) includes λ_KL and λ_TD, which are manually tuned. Rephrase to say there is no manual balancing among feedback types.
[Table 3] The column headers in the supplementary tables are inconsistent and hard to parse (e.g., repeated 'D' and 'PR'). Use explicit labels such as D, R, P, S, D+R, etc.
[Section 4] In the preferences likelihood, 'exp βR(ξ1)' should be 'exp(β R(ξ1))' for readability.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the variational derivation and algorithm are self-contained; the synthetic-feedback evaluation is in-sample with respect to the paper's likelihood models, but that is a validity gap, not a definitional reduction.

full rationale

MAVRL's derivation chain is not circular. Section 3 poses reward learning as Bayesian inference over R; Section 4 defines likelihoods for preferences (Bradley-Terry), demonstrations (Boltzmann-rational), ratings (ordered logit), and stops (cumulative-regret hazard); Section 5 derives the ELBO objective with a TD regularizer and a scalable encoder-decoder algorithm. Each of these steps is an independent modeling/algorithmic construction, and the claims about the objective do not assume the experimental conclusions. The main weakness is external validity: Appendix A.2 generates synthetic feedback using exactly the same likelihood families as Section 4 (e.g., preferences via sigma(beta*(Rbar(xi1)-Rbar(xi2))) and stops via h_t = 1 - exp(-lambda*R_t)), so Tables 1-2 and the complementarity analysis confirm that MAVRL can invert its own generative process. Section 7 acknowledges this ('we rely on synthetically generating feedback'). This is a validation gap, not a reduction of a prediction to its input: nothing in the ELBO or the algorithm forces the reported performance improvements, and the robustness experiments use genuine environment perturbations (action noise, link-length ratio, gravity/wind) that are external to the likelihood models. Citations to prior work, including Chan and van der Schaar (2021) for the TD regularizer and the authors' own Metz et al. (2025/2023), are descriptive/related-work references and are not load-bearing premises of the derivation.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical entities. The free parameters are mainly simulation hyperparameters and loss weights. The key assumptions are the generative feedback models and the mean-field variational approximation.

free parameters (5)

λ_KL, λ_TD = selected from {0.5, 1.0} per environment
Appendix A.1: best-performing setting chosen on the evaluation metric; these are loss weights in Eq. (4).
Inverse temperatures β_pref, β_demo = 5.0 for preferences; 10.0 (grids) / 5.0 (control) for demonstrations
Appendix A.2: hand-set rationality parameters in feedback simulation and likelihoods.
Stop model parameters c, ρ, reference percentile = c=1.0, ρ=0.1, 50th percentile
Appendix A.2: calibration of hazard model; λ = c / R_ref.
Segment lengths and trajectory rationality β_traj = L=10/32; β_traj ∈ {0, 1, 5}
Appendix A.2: simulation choices that shape the data distribution.
Rating cutpoints ψ_k = quantile-based in simulation, learned in model
Section 4: ordered-logit cutpoints; in simulation assigned by quantiles of returns.

assumptions (4)

domain assumption Feedback is generated by reward-rational choice models (Boltzmann-rational demonstrations, Bradley-Terry preferences, ordered-logit ratings, hazard-model stops)
Section 4 defines these likelihoods; Appendix A.2 uses them for simulation, making the evaluation self-consistent.
domain assumption The variational posterior factorizes over local rewards qθ(R|s,a,s′), ignoring correlations across transitions
Section 5.1 parameterizes a per-transition Gaussian; inherited from AVRIL.
domain assumption The auxiliary Qϕ approximates the optimal Q* required by demonstration and stop likelihoods
Section 5.1 uses Qϕ in the decoders; the TD loss encourages consistency but does not guarantee optimality.
ad hoc to paper Standard normal prior on reward values
Section 5.1: the prior p(R) = N(0, I) is chosen without empirical justification.

how reviews work

0 comments

Cite this review

Pith. "Pith review of MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference." pith.science (2026). https://pith.science/paper/4IND3MJH

@misc{pith2026260215206,
  author       = {Pith},
  title        = {Pith review of: MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4IND3MJH}},
  note         = {Machine review of arXiv:2602.15206}
}

read the original abstract

Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly learn reward functions from heterogeneous feedback types such as demonstrations, comparisons, ratings, and stops that provide qualitatively different signals. We address this challenge by formulating reward learning from multiple feedback types as Bayesian inference over a shared latent reward function, where each feedback type contributes information through an explicit likelihood. We introduce a scalable amortized variational inference approach that learns a shared reward encoder and feedback-specific likelihood decoders and is trained by optimizing a single evidence lower bound. Our approach avoids reducing feedback to a common intermediate representation and eliminates the need for manual loss balancing. Across discrete and continuous-control benchmarks, we show that jointly inferred reward posteriors outperform single-type baselines, exploit complementary information across feedback types, and yield policies that are more robust to environment perturbations. The inferred reward uncertainty further provides interpretable signals for analyzing model confidence and consistency across feedback types.

Figures

Figures reproduced from arXiv: 2602.15206 by the authors.

**Figure 1.** Overview of MAVRL. A shared variational reward encoder predicts reward samples from state–action pairs for each feedback modality. A jointly optimized Q-value model estimates optimal values for the same transitions. Both reward samples and Q-values are passed to modality-specific decoders, which maximize their respective likelihoods. A KL-divergence regularizer enforces consistency with a standard normal prior, whil… view at source ↗

**Figure 2.** Visualizations of inferred reward functions from [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Normalized mean returns (n = 10) of policies trained on rewards inferred by each method under three dynamics perturbation scenarios. Reward models and baselines are trained in the unperturbed setting and remain fixed throughout the variations. Error bars denote standard error. (a) Increasing environmental stochasticity in grid_cliff. (b) Increasing ratio between pendulum handle lengths in Acrobot-v1. (c) Increasing … view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Normalized mean returns (n = 10) of policies trained on rewards inferred by each method under three dynamics perturbation scenarios. Reward models and baselines are trained in the unperturbed setting and remain fixed throughout the variations. Returns are normalized su…

**Figure 5.** Figure 5: grid_sparse: Visualizations of inferred reward functions from 2 demonstrations, 256 pairwise comparisons, or 128 ratings on a 10 × 10 grid_sparse environment. The final column shows the result obtained when combining all feedback modalities. (a) Joint visual encoding …

**Figure 6.** Figure 6: grid_cliff: Visualizations of inferred reward functions from 2 demonstrations, 256 pairwise comparisons, or 128 ratings on a 10 × 10 grid_cliff environment. The final column shows the result obtained when combining all feedback modalities. (a) Joint visual encoding of …

**Figure 7.** Figure 7: grid_trap: Visualizations of inferred reward functions from 2 demonstrations, 256 pairwise comparisons, or 128 ratings on a 10 × 10 grid_trap environment. The final column shows the result obtained when combining all feedback modalities.. (a) Joint visual encoding of m…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

19 extracted references · 3 canonical work pages

[4]

We now summarize the environmental perturbations used to evaluate the robustness of downstream policy performance under systematic changes in environment dynamics. Grid-World Environments (grid_cliff, grid_sparse, grid_trap).For the grid-world environments, we perturb the transition dynamics by introducing stochasticity in the agent’s action execution. Sp...

1999
[11]

MaÃG, l Macuglia, Paul Friedrich, and Giorgia Ramponi

URL http://arxiv.org/abs/1711.05101. MaÃG, l Macuglia, Paul Friedrich, and Giorgia Ramponi. Fine-tuning behavioral cloning policies with preference- based reinforcement learning.arXiv preprint arXiv:2509.26605,

arXiv
[13]

URL https://doi.org/10.1145/3623384

doi: 10.1145/3623384. URL https://doi.org/10.1145/3623384. Yannick Metz, David Lindner, Raphaël Baur, Daniel A. Keim, and Mennatallah El-Assady. RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback. InInteractive Learning with Implicit Human Feedback Workshop at ICML, Honolulu, Hawaii, USA,

doi:10.1145/3623384
[14]

RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback

doi: 10.48550/ARXIV.2308.04332. Yannick Metz, Andras Geiszl, Raphaël Baur, and Mennatallah El-Assady. Reward learning from multiple feedback types.International Conference on Learning Representations,

work page Pith review arXiv doi:10.48550/arxiv.2308.04332
[15]

Vivek Myers, Erdem Bıyık, Nima Anari, and Dorsa Sadigh

URL https://openreview.net/forum?id= 9Ieq8jQNAl. Vivek Myers, Erdem Bıyık, Nima Anari, and Dorsa Sadigh. Learning Multimodal Rewards from Rankings. Conference on Robot Learning (CoRL) 2021, October

2021
[17]

arXiv:2203.02155 [cs]

URL http://arxiv.org/abs/2203.02155. arXiv:2203.02155 [cs]. Malayandi Palan, Nicholas C Landolfi, Gleb Shevchuk, and Dorsa Sadigh. Learning reward functions by in- tegrating human demonstrations and preferences.Robotics: Science and Systems 2019,

arXiv 2019
[19]

Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques

URL http://arxiv.org/abs/1912.01703. Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques. Personalizing reinforcement learning from human feedback with variational preference learning. InProceedings of the 38th International Conference on Neural Information Processing Systems, NIPS ’24, Red Hook, NY, USA,

arXiv 1912
[20]

13 A Implementation Details A.1 Model Architecture The reward encoder qθ and Q-value estimator Qϕ were implemented as two-layer MLPs with Leaky ReLU activations

URL https://openreview.net/ forum?id=WesY0H9ghM. 13 A Implementation Details A.1 Model Architecture The reward encoder qθ and Q-value estimator Qϕ were implemented as two-layer MLPs with Leaky ReLU activations. For grid environments, we used 64 hidden units per layer, learning rate 5×10 −4, batch size 32, and state-only rewards R(s). For control tasks (Ca...

2017

Show all 19 references

[1980]

Shaunak A

doi: https://doi.org/10.1111/j.2517-6161.1980.tb01109.x. Shaunak A. Mehta and Dylan P. Losey. Unified learning from demonstrations, corrections, and preferences during physical human–robot interaction.J. Hum.-Robot Interact., 13(3), August

1980
[2004]

ISBN 978-1-58113-838-2

Association for Computing Machinery. ISBN 978-1-58113-838-2. doi: 10.1145/1015330.1015430. URL https://dl.acm.org/ doi/10.1145/1015330.1015430. Ahmed Abouelazm, Jonas Michel, and J. Marius Zöllner. A review of reward functions for reinforcement learning in the context of auton...
[2009]

ISBN 978-1-60558-658-8

Association for Computing Machinery. ISBN 978-1-60558-658-8. doi: 10.1145/1597735. 1597738. URLhttps://dl.acm.org/doi/10.1145/1597735.1597738. W Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, and Peter Stone. Reward (mis) design for autonomous driving.Artific...
[2014]

doi: 10.48550/arXiv.1312.6114. 11 W. Bradley Knox and Peter Stone. Interactively shaping agents via human reinforcement: The TAMER framework. InProceedings of the fifth international conference on Knowledge capture, K-CAP ’09, pages 9–16, New York, NY, USA, September

doi arXiv
[2017]

Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt

ISBN 9780999241103. Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt. Unsolved problems in ML safety.arXiv preprint arXiv:2109.13916,

arXiv
[2020]

arXiv:2002.04833 [cs]

URLhttp://arxiv.org/abs/2002.04833. arXiv:2002.04833 [cs]. Diederik P. Kingma and Max Welling. Auto-Encoding Variational Bayes. In2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings,

2002 arXiv
[2021]

URL http://arxiv

doi: 10.48550/arXiv.2109.12750. URL http://arxiv. org/abs/2109.12750. Andrew Y. Ng, Daishi Harada, and Stuart J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. InProceedings of the 16th International Conference on Machine Learning,

doi arXiv
[2022]

doi: 10.1177/02783649211041652

ISSN 0278-3649. doi: 10.1177/02783649211041652. URL https://doi.org/10.1177/02783649211041652. Publisher: SAGE Publica- tions Ltd STM. Yuang Cai, Yuyu Yuan, Jinsheng Shi, and Qinhong Lin. Approximated variational bayesian inverse reinforcement learning for large language model...

doi
[2023]

doi: 10.1609/aaai.v37i5.25740

ISBN 978-1-57735-880-0. doi: 10.1609/aaai.v37i5.25740. URLhttps://doi.org/10.1609/aaai.v37i5.25740. Adam Gleave, Michael D Dennis, Shane Legg, Stuart Russell, and Jan Leike. Quantifying differences in reward functions. InInternational Conference on Learning Representations,

doi
[2024]

Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané

doi: 10.1109/IV55156.2024.10588385. Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. Concrete problems in AI safety.arXiv preprint arXiv:1606.06565,

2024
[2025]

Scalable Bayesian inverse reinforcement learning

Alex James Chan and Mihaela van der Schaar. Scalable Bayesian inverse reinforcement learning. InInternational Conference on Learning Representations 2021,

2021

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.

[1] [4]

We now summarize the environmental perturbations used to evaluate the robustness of downstream policy performance under systematic changes in environment dynamics. Grid-World Environments (grid_cliff, grid_sparse, grid_trap).For the grid-world environments, we perturb the transition dynamics by introducing stochasticity in the agent’s action execution. Sp...

1999

[2] [11]

MaÃG, l Macuglia, Paul Friedrich, and Giorgia Ramponi

URL http://arxiv.org/abs/1711.05101. MaÃG, l Macuglia, Paul Friedrich, and Giorgia Ramponi. Fine-tuning behavioral cloning policies with preference- based reinforcement learning.arXiv preprint arXiv:2509.26605,

arXiv

[3] [13]

URL https://doi.org/10.1145/3623384

doi: 10.1145/3623384. URL https://doi.org/10.1145/3623384. Yannick Metz, David Lindner, Raphaël Baur, Daniel A. Keim, and Mennatallah El-Assady. RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback. InInteractive Learning with Implicit Human Feedback Workshop at ICML, Honolulu, Hawaii, USA,

doi:10.1145/3623384

[4] [14]

RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback

doi: 10.48550/ARXIV.2308.04332. Yannick Metz, Andras Geiszl, Raphaël Baur, and Mennatallah El-Assady. Reward learning from multiple feedback types.International Conference on Learning Representations,

work page Pith review arXiv doi:10.48550/arxiv.2308.04332

[5] [15]

Vivek Myers, Erdem Bıyık, Nima Anari, and Dorsa Sadigh

URL https://openreview.net/forum?id= 9Ieq8jQNAl. Vivek Myers, Erdem Bıyık, Nima Anari, and Dorsa Sadigh. Learning Multimodal Rewards from Rankings. Conference on Robot Learning (CoRL) 2021, October

2021

[6] [17]

arXiv:2203.02155 [cs]

URL http://arxiv.org/abs/2203.02155. arXiv:2203.02155 [cs]. Malayandi Palan, Nicholas C Landolfi, Gleb Shevchuk, and Dorsa Sadigh. Learning reward functions by in- tegrating human demonstrations and preferences.Robotics: Science and Systems 2019,

arXiv 2019

[7] [19]

Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques

URL http://arxiv.org/abs/1912.01703. Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques. Personalizing reinforcement learning from human feedback with variational preference learning. InProceedings of the 38th International Conference on Neural Information Processing Systems, NIPS ’24, Red Hook, NY, USA,

arXiv 1912

[8] [20]

13 A Implementation Details A.1 Model Architecture The reward encoder qθ and Q-value estimator Qϕ were implemented as two-layer MLPs with Leaky ReLU activations

URL https://openreview.net/ forum?id=WesY0H9ghM. 13 A Implementation Details A.1 Model Architecture The reward encoder qθ and Q-value estimator Qϕ were implemented as two-layer MLPs with Leaky ReLU activations. For grid environments, we used 64 hidden units per layer, learning rate 5×10 −4, batch size 32, and state-only rewards R(s). For control tasks (Ca...

2017