Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment

Jinsheng Shi; Qinhong Lin; Yuang Cai; Yuyu Yuan

REVIEW 4 major objections 6 minor 35 references

Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read By casting LLM alignment as Bayesian inverse reinforcement learning, AVA fits a per-token reward distribution for each demonstration and reports higher reward accuracy and win rates than Bradley-Terry, Preference Transformer, DPO, and AfD.

desk verdict Plausible new alignment objective, but the headline 'outperforms' claim is not statistically supported and the key derivation is missing; worth a serious referee after revision. read the letter →

arxiv 2411.09341 v1 pith:DSGXPH6A submitted 2024-11-14 cs.LG

Yuang Cai , Yuyu Yuan , Jinsheng Shi , Qinhong Lin This is my paper · ORCID

classification cs.LG

keywords largelanguagemodelalignmentBayesianinversereinforcementlearningvariationalinferencerewardmodelingdirectpreferenceoptimizationintermediateTD-errorconstrainttransformerarchitectures

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 proposes a training objective called Approximated Variational Alignment (AVA) for aligning large language models with human feedback. Instead of learning a scalar reward difference between a chosen and a rejected sentence, AVA treats the alignment task as Bayesian inverse reinforcement learning and models a per-token reward distribution for every single demonstration. The authors argue that this makes fuller use of the feedback signal, supports intermediate rewards, and reduces reward hacking. They report that AVA outperforms Bradley-Terry, Preference Transformer, DPO, and AfD in reward modeling, RL fine-tuning, and direct optimization on standard preference and demonstration datasets.

What carries the argument

The load-bearing machinery is the Approximated Variational Reward Imitation Learning (AVRIL) objective for Bayesian inverse reinforcement learning, adapted to autoregressive text generation. The adaptation rests on three per-token terms: the log-probability of the ground-truth token under a Boltzmann policy built from a Q-function, a KL term that keeps the reward distribution near its prior, and a TD-error constraint that forces the Q-values' temporal-difference error to match the sampled Gaussian reward. The TQR architecture operationalizes this by adding a Q-value head and a mean-variance reward head to a pre-trained Transformer decoder, with attention-based weights re-weighting each token's reward. For preference data, the paper adds a Contrastive Bayesian inverse RL term and a Contrastive Expected Return objective so the chosen demonstration's reward exceeds the rejected one's.

What would settle it

A direct test would train AVA-d on demonstrations whose tokens are randomly permuted (so no true intermediate reward structure exists) and then evaluate the learned reward on held-out preference pairs. If the model still achieves high reward accuracy, the TD-error constraint is not in fact identifying a meaningful reward and the gains must come from the policy objective alone.

Watch

Extended reading notes

Core claim

The central discovery the paper is trying to establish is that the reward function for aligning a language model is better learned as a posterior distribution over per-token rewards than as a scalar comparison between one whole response and another. In the AVA framework, the demonstration likelihood is expressed through a Boltzmann policy over a Q-function, and the reward is constrained to equal the temporal-difference error of that Q-function at each step. Optimizing the evidence lower bound of the Bayesian inverse RL posterior with this constraint yields an objective that directly models the reward of every single token and of every single demonstration, without requiring pairwise preferences. On the reported benchmarks, this objective outperforms the standard Bradley-Terry and Preference Transformer reward models and the DPO and AfD direct optimizers, in both reward accuracy and LLM win rates.

Load-bearing premise

The load-bearing premise is that the true reward can be recovered by forcing the temporal-difference error of a Q-function fit to the demonstrations to match the learned reward; without any independent reward signal, the reward distribution is identified only by model choice and initialization, not by the data.

Editorial extensions

If this is right

AVA-d learns a reward from single demonstrations, so LLM alignment no longer requires pairwise preference labels.
Token-level intermediate rewards should generalize better to data with similar intermediate state distributions but different complete sentences.
Because the policy and the reward are trained jointly under a TD-error consistency constraint, the learned reward is less likely to separate from policy behavior, reducing reward hacking in downstream RL.
The same AVA objective can drive both a reward-modeling pipeline (with a small backbone and downstream PPO) and a direct-optimization pipeline (with a large policy), replacing separate Bradley-Terry and DPO/AfD objectives.

Reading between the lines

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

One open question the paper leaves implicit is whether the learned 'reward' is identifiable: in the demonstration-only setting, no independent reward signal exists, so the reward distribution may simply reflect the Q-function parameterization and initialization rather than a ground-truth reward.
A natural extension is to apply the per-token TD-error credit assignment to other dense-feedback-free sequential domains, such as sparse-reward robot control, where the same variational machinery could distribute credit across actions.
Because the paper's ablations show the TD-error constraint (the 'irl' term) matters, an explicit comparison against a per-token Bradley-Terry variant with dense rewards would isolate whether the benefit comes from dense rewards or from the variational posterior itself.

Share X Bluesky LinkedIn Reddit HN

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

The paper proposes AVA, an AVRIL/BIRL-style objective for LLM alignment that models token-level intermediate rewards and works on both preference and demonstration data. It's a reasonable synthesis of AVRIL, Preference Transformer's attention weighting, and contrastive learning. But as it stands, the empirical claim that AVA outperforms existing approaches is not established: the paper's own reproducibility checklist says no variance or significance tests, and several reported gains are tiny (e.g., Table 1 Harmless GPT-2 70.27 vs 70.02; Table 2 Harmless BoN 28.8 vs 28.6). Those could easily be seed noise. The central derivation for the contrastive preference objective (Eq. 15 to Eq. 16) is deferred to a Technical Appendix that is not in the preprint, so the BIRL framing can't be verified from the available text.

What is new and good: the idea of directly modeling the reward per demonstration instead of just the reward difference, plus intermediate rewards, is a sensible direction. The TQR architecture with reward and Q-value heads is a clean way to implement it. The authors are honest about the missing variance measures in the checklist, and they provide ablations that isolate the components. The method is flexible: it can be used for reward modeling or direct optimization, and on demonstration or preference data. That flexibility is genuinely useful.

The soft spots: the lack of error bars and statistical tests is the load-bearing flaw, because the strongest claims rest on small deltas. Some differences are larger (e.g., Summary BART accuracy 64.31 vs 59.27; PPO Summary win rate 33.9 vs 18.5), so the method may indeed help, but the paper as written overclaims. The missing appendix makes the derivation unverifiable; that should be fixed before publication. The TD-error reward is defined via a jointly trained Q-function, so the 'true reward' is not independently identified — that's a conceptual limitation, but it's secondary to the empirical issue; the method could still work as a regularizer even if the reward interpretation is loose. Eq. 14's notation is ambiguous (looks like a division by a gamma-exponentiated softmax when it should be a difference in log-space).

Who it's for: researchers working on RLHF/DPO alternatives, especially those interested in token-level rewards and IRL-based alignment. It deserves a serious referee, but it needs a major revision with proper statistics and the missing derivation. I'd bring it to a reading group for discussion, but I wouldn't cite it yet in its current form.

Recommendation: send to peer review, but the authors should be told that acceptance hinges on adding variance/statistical tests and the technical appendix.

Referee Report

4 major / 6 minor

Summary. The paper proposes Approximated Variational Alignment (AVA), a Bayesian Inverse Reinforcement Learning (BIRL) objective for LLM alignment. AVA is instantiated in two forms: AVA-d for demonstration data and AVA-p for preference data, both built on an Approximated Variational Reward Imitation Learning (AVRIL) objective. The method introduces token-level intermediate rewards, a Transformer architecture with Q-value and reward heads (TQR), and supports both reward-modeling and direct-optimization pipelines. Experiments on Anthropic-Harmless, Anthropic-Helpful, OpenAI-Summary, Alpaca-GPT-4, and Math-GPT-4o report reward accuracy and win rates against Bradley-Terry, Preference Transformer, DPO, and AfD baselines, with ablation studies. The central claim is that AVA outperforms existing alignment approaches in reward modeling, RL fine-tuning, and direct optimization.

Significance. If the results hold, the paper would make a useful contribution by reframing LLM alignment as BIRL, enabling token-level intermediate reward modeling and direct reward estimation on individual demonstrations rather than only pairwise reward differences. The approach is flexible across data formats and training pipelines, and the ablation study is a genuine strength. The paper ships pseudocode and claims code availability, and it evaluates on standard public datasets against common baselines, which is good empirical practice in principle. However, the significance is currently limited by two load-bearing issues: the central ELBO derivation is deferred to an absent Technical Appendix, and the empirical comparisons lack any measure of variation or statistical testing, so the headline superiority claim is not established from the reported numbers.

major comments (4)

[Eq. 15-16, §Alignment from Preference] The derivation of Eq. 16 from Eq. 15 is deferred to a Technical Appendix that is not present in the preprint. This is not a routine algebraic step: minimizing the sum of two KL divergences with respect to p(R|P+) and p(R|P−) is claimed to yield an ELBO containing log[1 − p(P−|R)], which requires independence or complementarity assumptions that are not stated. Moreover, the set P− used in Eq. 15 is not formally defined (the text says it denotes demonstrations that differ from P−). Because Eq. 16 is the basis for the AVA-p objective in Eq. 17, the theoretical foundation of the method is not verifiable from the manuscript as written.
[Reproducibility Checklist; Tables 1-5] The empirical claim that AVA outperforms baselines is not statistically supported. The Reproducibility Checklist explicitly answers 'no' to including measures of variation and 'no' to using statistical significance tests, and all tables report single numbers without confidence intervals or error bars. Several reported gains are small enough to be seed noise: Table 1 (Harmless, GPT-2) shows 70.27 vs 70.02 for AVA-p vs Bradley-Terry (Δ=0.25); Table 2 (Harmless BoN) shows 28.8 vs 28.6 (Δ=0.2); Table 3 (Helpful PPO) shows 1.8 vs 1.0 (Δ=0.8); Table 4 (Harmless direct optimization) shows 13.7 vs 12.5 (Δ=1.2). Without paired tests or variance estimates, the abstract's claim that 'AVA outperforms existing LLM alignment approaches' is not established for these settings.
[Eq. 13, §AVA-d] In Eq. 13, the term log B(yt+1|y1:t; Qθ) from Eq. 5 is replaced by β log softmax(απw(yt+1|y1:t)), omitting the log-partition function of the Boltzmann distribution, −log Σ_{y′} exp(β Qw(y1:t, y′)). Because Qw is itself a log-softmax (Eq. 12), this omitted term is not a constant independent of the policy parameters, and no argument is given for why this approximation leaves the optimization objective qualitatively unchanged. Since this substitution is used in both AVA-d and AVA-p, the soundness of the training objective depends on this unstated approximation.
[Eq. 4-7, §Preliminaries and §AVA-d] The reward distribution qφ is trained to match the TD error of a Q-function that is simultaneously fit to the demonstration likelihood. In the demonstration-only setting there is no independent reward signal, so the learned reward is operationally defined by the parameterization and initialization rather than identified as the 'true reward' stated in the Abstract and Section 'Alignment from Demonstration'. This is a correctness-risk concern: if the TD-error constraint does not identify a unique reward, the claim of direct reward modeling is unsupported even if the empirical win rates hold. The paper would benefit from a formal identifiability argument or a sensitivity analysis over initializations.

minor comments (6)

[Eq. 15 and surrounding text] The notation for negative demonstrations is confusing: the text defines P− as 'demonstrations that differ from P−', but Eq. 15 and the preceding sentence use P− inconsistently, and P− is never formally defined as a set complement or otherwise.
[Eq. 17] Eq. 17 uses δt(θ) in the TD-error term, but in the preference setting the Q-function is parameterized through the policy πw (Eq. 14), so the notation should be δt(w); this inconsistency makes it unclear which parameters are being updated in the term.
[Eq. 11] The definition of the reward weight wt in Eq. 11 is hard to parse: the index t appears both as the summation index and as the output index, and the softmax normalization is not expressed clearly; please rewrite with distinct indices.
[Table 1] The rows 'AVA-p w/o neg' and 'AVA-d' have identical accuracy values for several columns (e.g., 70.54, 70.36, 69.75, 69.15, 62.06, 58.65/59.00), which looks like a copy-paste artifact or a coincidence that should be clarified.
[References] The AfD baseline is cited as Sun and van der Schaar (2024) 'Inverse-RLignment', but the text calls it 'Alignment from Demonstrations (AfD)'; please ensure the reference name and acronym match the cited work.
[Reproducibility Checklist] The checklist states that all source code is included in a code appendix and will be made public, but no link or anonymized repository is provided in the manuscript; please add a URL or clearly state where the code will be available.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: AVA is evaluated against external baselines on public datasets, and its training objectives derive from external AVRIL/BIRL work without author-overlap self-citations.

full rationale

The paper's derivation chain is not circular. AVA-d and AVA-p are direct natural-language-generation adaptations of the AVRIL objective (Eqs. 4, 5, 13, 17), cited to Chan and van der Schaar, who are not among the present authors and whose method is external prior work. The claimed advantage over Bradley-Terry, Preference Transformer, DPO, and AfD is supported by accuracy and win-rate comparisons on public datasets (Tables 1-5), and none of those reported evaluation quantities is defined by the AVA training loss itself. The only caveat is that the learned reward distribution q_phi is constrained to match the TD error of the jointly trained Q-function (Eq. 4 third term; Eqs. 5 and 13 third term), so the 'true reward' is not independently identified; this is an identifiability and validity limitation rather than circularity, because the reported benchmark outcomes are not entailed by that constraint. The Reproducibility Checklist's 'no' answers for measures of variation and significance tests weaken the statistical support for the empirical claims but do not make the claims true by construction. No load-bearing self-citation, no imported uniqueness theorem, and no ansatz smuggled in through the authors' own prior work appears in the paper.

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

The central claim rests on hyperparameters β, λ, γ, α whose values are not reported, and on domain assumptions about the NLG-as-MDP framing, Gaussian rewards, the deferred CBIRL ELBO, the centrality of the last token position, and the dropped Boltzmann partition function. The TQR architecture adds heads, but these are parameterizations, not new physical entities.

free parameters (4)

β = not reported in main text
Boltzmann temperature in Eq. 3/13 that converts Q-values to token probabilities; a hand-chosen hyperparameter.
λ = not reported in main text
Weight of the TD-error constraint in Eq. 4/5/13/17; a hand-chosen hyperparameter.
γ = not reported in main text
Discount factor in the TD error (Eq. 7/14); a hand-chosen hyperparameter.
α = not reported in main text
Temperature scaling policy probabilities to Q-values in Eq. 12; a hand-chosen hyperparameter.

assumptions (5)

domain assumption NLG can be modeled as an MDP with deterministic state transitions.
Text generation is treated as a token-level MDP; reward is a function of the next state. Stated in the Preliminaries section.
domain assumption The reward distribution qφ is Gaussian at each token (Eq. 10).
Reward head outputs mean and std; expected reward is used in CER and RL objectives.
ad hoc to paper The ELBO for CBIRL (Eq. 16) is a valid bound on Eq. 15, with p(R|P-) interpreted as the posterior over rewards that motivate behaviors differing from the rejected set.
The derivation is deferred to the Technical Appendix, which is not in the v1 text; the construction of p(R|P-) is non-standard.
domain assumption The last-position reward is decisive for the overall return, justifying the CER objective that only compares final-step rewards (Eq. 18).
The paper cites prior work on factual recall to argue the last Transformer position gathers most knowledge.
ad hoc to paper The approximation of log B by β log softmax(απw) in Eq. 13, dropping the Boltzmann partition function, does not change the optimization significantly.
Eq. 4's log B includes a normalizer over the vocabulary; Eq. 13 omits it.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment." pith.science (2026). https://pith.science/paper/DSGXPH6A

@misc{pith2026241109341,
  author       = {Pith},
  title        = {Pith review of: Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DSGXPH6A}},
  note         = {Machine review of arXiv:2411.09341}
}

read the original abstract

The alignment of large language models (LLMs) is crucial for generating helpful and harmless content. Existing approaches leverage preference-based human feedback data to learn the reward function and align the LLM with the feedback data. However, these approaches focus on modeling the reward difference between the chosen and rejected demonstrations, rather than directly modeling the true reward from each demonstration. Moreover, these approaches assume that the reward is only obtained at the end of the sentence, which overlooks the modeling of intermediate rewards. These issues lead to insufficient use of training signals in the feedback data, limiting the representation and generalization ability of the reward and potentially resulting in reward hacking. In this paper, we formulate LLM alignment as a Bayesian Inverse Reinforcement Learning (BIRL) problem and propose a novel training objective, Approximated Variational Alignment (AVA), to perform LLM alignment through Approximated Variational Reward Imitation Learning (AVRIL). The BIRL formulation facilitates intermediate reward modeling and direct reward modeling on each single demonstration, which enhances the utilization of training signals in the feedback data. Experiments show that AVA outperforms existing LLM alignment approaches in reward modeling, RL fine-tuning, and direct optimization.

Figures

Figures reproduced from arXiv: 2411.09341 by the authors.

**Figure 1.** Overview of the TQR architecture. TQR Architecture The original AVRIL adopts the architecture with a reward encoder and a Q-value decoder. To compute the AVA-d training objective and leverage the pre-trained weights of the backbone transformer model, we add a reward head and a Qvalue head at the top of the Transformer decoder, as shown in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

35 extracted references · 5 canonical work pages

[1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al

Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774

arXiv 2023
[4]

Bai, Y.; Jones, A.; Ndousse, K.; Askell, A.; Chen, A.; DasSarma, N.; Drain, D.; Fort, S.; Ganguli, D.; Henighan, T.; et al. 2022. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862

arXiv 2022
[5]

A.; and Terry, M

Bradley, R. A.; and Terry, M. E. 1952. Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39(3/4): 324--345

1952
[6]

J.; and van der Schaar, M

Chan, A. J.; and van der Schaar, M. 2021. Scalable B ayesian Inverse Reinforcement Learning. In International Conference on Learning Representations

work page 2021
[7]

Coste, T.; Anwar, U.; Kirk, R.; and Krueger, D. 2023. Reward model ensembles help mitigate overoptimization. arXiv preprint arXiv:2310.02743

arXiv 2023
[8]

Gao, L.; Schulman, J.; and Hilton, J. 2023. Scaling laws for reward model overoptimization. In International Conference on Machine Learning, 10835--10866. PMLR

2023

Show all 35 references

[9]

Geva, M.; Bastings, J.; Filippova, K.; and Globerson, A. 2023. Dissecting recall of factual associations in auto-regressive language models. arXiv preprint arXiv:2304.14767

2023 arXiv
[10]

Hanna, M.; Liu, O.; and Variengien, A. 2024. How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model. Advances in Neural Information Processing Systems, 36

2024
[11]

Holtzman, A.; Buys, J.; Du, L.; Forbes, M.; and Choi, Y. 2019. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751

2019 arXiv
[12]

Kim, C.; Park, J.; Shin, J.; Lee, H.; Abbeel, P.; and Lee, K. 2023. Preference transformer: Modeling human preferences using transformers for RL. arXiv preprint arXiv:2303.00957

2023 arXiv
[13]

Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602

2013 arXiv
[14]

Nakano, R.; Hilton, J.; Balaji, S.; Wu, J.; Ouyang, L.; Kim, C.; Hesse, C.; Jain, S.; Kosaraju, V.; Saunders, W.; et al. 2021. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332

2021 arXiv
[15]

Y.; Russell, S.; et al

Ng, A. Y.; Russell, S.; et al. 2000. Algorithms for inverse reinforcement learning. In Icml, volume 1, 2

2000
[16]

OpenAI. 2022. Introducing ChatGPT. https://openai.com/index/chatgpt/

2022
[17]

Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730--27744

2022
[18]

D.; Ermon, S.; and Finn, C

Rafailov, R.; Sharma, A.; Mitchell, E.; Manning, C. D.; Ermon, S.; and Finn, C. 2024. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36

2024
[19]

Ramachandran, D.; and Amir, E. 2007. Bayesian Inverse Reinforcement Learning. In IJCAI, volume 7, 2586--2591

2007
[20]

Ranzato, M.; Chopra, S.; Auli, M.; and Zaremba, W. 2016. Sequence level training with recurrent neural networks. In 4th International Conference on Learning Representations, ICLR 2016

2016
[21]

Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347

2017 arXiv
[22]

Shen, T.; Jin, R.; Huang, Y.; Liu, C.; Dong, W.; Guo, Z.; Wu, X.; Liu, Y.; and Xiong, D. 2023. Large language model alignment: A survey. arXiv preprint arXiv:2309.15025

2023 arXiv
[23]

Skalse, J.; Howe, N.; Krasheninnikov, D.; and Krueger, D. 2022. Defining and characterizing reward gaming. Advances in Neural Information Processing Systems, 35: 9460--9471

2022
[24]

Song, F.; Yu, B.; Li, M.; Yu, H.; Huang, F.; Li, Y.; and Wang, H. 2024. Preference ranking optimization for human alignment. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, 18990--18998

2024
[25]

Stiennon, N.; Ouyang, L.; Wu, J.; Ziegler, D.; Lowe, R.; Voss, C.; Radford, A.; Amodei, D.; and Christiano, P. F. 2020. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33: 3008--3021

2020
[26]

Sun, H.; and van der Schaar, M. 2024. Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment. arXiv preprint arXiv:2405.15624

2024 arXiv
[27]

T.; and Pal, C

Tay, Y.; Ong, D.; Fu, J.; Chan, A.; Chen, N.; Luu, A. T.; and Pal, C. 2020. Would you rather? a new benchmark for learning machine alignment with cultural values and social preferences. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics,...

2020
[28]

Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288

2023 arXiv
[29]

N.; Kaiser, .; and Polosukhin, I

Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, .; and Polosukhin, I. 2017. Attention is all you need. Advances in neural information processing systems, 30

2017
[30]

Weidinger, L.; Mellor, J.; Rauh, M.; Griffin, C.; Uesato, J.; Huang, P.-S.; Cheng, M.; Glaese, M.; Balle, B.; Kasirzadeh, A.; et al. 2021. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359

2021 arXiv
[31]

Yang, A.; Xiao, B.; Wang, B.; Zhang, B.; Bian, C.; Yin, C.; Lv, C.; Pan, D.; Wang, D.; Yan, D.; et al. 2023. Baichuan 2: Open large-scale language models. arXiv preprint arXiv:2309.10305

2023 arXiv
[32]

Yuan, Z.; Yuan, H.; Tan, C.; Wang, W.; Huang, S.; and Huang, F. 2023. Rrhf: Rank responses to align language models with human feedback without tears. arXiv preprint arXiv:2304.05302

2023 arXiv
[33]

Zhang, X.; Ton, J.-F.; Shen, W.; Wang, H.; and Liu, Y. 2024. Overcoming reward overoptimization via adversarial policy optimization with lightweight uncertainty estimation. arXiv preprint arXiv:2403.05171

2024 arXiv
[34]

Zhang, Y.; Sun, S.; Galley, M.; Chen, Y.-C.; Brockett, C.; Gao, X.; Gao, J.; Liu, J.; and Dolan, B. 2019. Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536

2019 arXiv
[35]

Zopf, M. 2018. Estimating summary quality with pairwise preferences. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 1687--1696

2018

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al

Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774

arXiv 2023

[4] [4]

Bai, Y.; Jones, A.; Ndousse, K.; Askell, A.; Chen, A.; DasSarma, N.; Drain, D.; Fort, S.; Ganguli, D.; Henighan, T.; et al. 2022. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862

arXiv 2022

[5] [5]

A.; and Terry, M

Bradley, R. A.; and Terry, M. E. 1952. Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39(3/4): 324--345

1952

[6] [6]

J.; and van der Schaar, M

Chan, A. J.; and van der Schaar, M. 2021. Scalable B ayesian Inverse Reinforcement Learning. In International Conference on Learning Representations

work page 2021

[7] [7]

Coste, T.; Anwar, U.; Kirk, R.; and Krueger, D. 2023. Reward model ensembles help mitigate overoptimization. arXiv preprint arXiv:2310.02743

arXiv 2023

[8] [8]

Gao, L.; Schulman, J.; and Hilton, J. 2023. Scaling laws for reward model overoptimization. In International Conference on Machine Learning, 10835--10866. PMLR

2023