REVIEW 3 major objections 6 minor 1 cited by
Towards Cost-Effective Reward Guided Text Generation
T0 review · 3 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash
Pith's one-line read FaRMA trains a reward model whose single forward pass scores every possible next token, and proves this makes decoding prefer prefixes of optimal responses.
desk verdict FaRMA's single-pass reward model gives a real, useful speedup in decoding-time alignment, but Theorem 3's guarantee is assumed rather than proven, and the empirical paper is stronger than the theory. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing object is the local optimal-extension constraint $V_\theta(y_{1:i}|x) = \max_{y_{i+1}} V_\theta(y_{1:i+1}|x)$, trained as the squared loss $L^{(b)}$. This turns the value function into a Q-function under the deterministic token transitions of language generation with no discount and reward only at the end: $Q^*([x,y_{1:i-1}], y_i) = \max_{y_{i+1}} Q^*([x,y_{1:i}], y_{i+1})$. Because the FaRMA architecture outputs scores for all next tokens in a single forward pass, the max over the vocabulary is computed with one call, making the constraint trainable and making decoding cost independent of the top-$k$ width.
What would settle it
Train FaRMA on a constructed preference dataset where an optimal response shares a prefix with several losing responses, as in the proof of Theorem 1, then check whether the learned value of the optimal prefix is lower than that of a suboptimal prefix that appears more often as a winner. Alternatively, after training, evaluate the squared max-consistency residual on prefixes reachable by the base policy; any nonzero residual on such prefixes violates the premise of Theorem 3.
Extended reading notes
Core claim
The paper's central claim is that a reward model can be trained so that, at every decoding step, a single forward pass through the prefix produces scores for every token in the vocabulary, and the score assigned to a prefix equals the value of its best possible completion. The training alternates a Bradley-Terry loss on full-sequence preference pairs with a squared max-consistency loss $L^{(b)} = \frac{1}{2}\big(V_\theta(y_{1:i}|x) - \max_{y_{i+1}} V_\theta(y_{1:i+1}|x)\big)^2$. The paper proves (Theorem 3) that if the max-consistency loss is driven to zero on the relevant prefixes, the learned value function scores every prefix of an optimal response at least as high as every other prefix, something the paper shows prior RGTG methods (PARGS, CD) provably fail to guarantee. Empirically, FaRMA requires about one sixth of the generation time of ARGS on TL;DR while achieving average reward competitive with DPO and PPO.
Load-bearing premise
The guarantee rests on the assumption that alternating the full-sequence preference loss and the max-consistency loss pushes the max-consistency term to exactly zero on every prefix the decoder will actually score; the paper invokes this in Theorem 3 but gives no proof that training reaches that fixed point.
Editorial extensions
If this is right
- FaRMA requires one reward-model call per decoding step instead of one per candidate token, cutting total model calls on TL;DR from roughly 657 for ARGS to roughly 107 for FaRMA and wall-clock generation from 32 minutes to 5 minutes.
- Under the assumption that training drives the max-consistency loss to zero on decoding prefixes, Theorem 3 guarantees the learned value function scores every prefix of an optimal response at least as high as every other prefix, so greedy decoding follows optimal completions.
- FaRMA matches or exceeds the average reward of DPO and PPO on TL;DR and HH without retraining the base language model, and it beats all RGTG baselines on reward while running faster.
- Because the reward model emits scores for the whole vocabulary in one pass, decoding cost no longer grows with the number of candidate tokens considered, so top-$k$ width can be increased without extra reward-model calls.
Reading between the lines
- Extension the paper leaves implicit: FaRMA's training scheme could be run with a full-sequence reward model supplying the preference scores instead of human labels, which would let the same architecture distill any existing reward model into a token-level Q-function.
- If the max-consistency loss is satisfied only approximately, Theorem 3's guarantee degrades to an approximation error proportional to the largest residual on decoding prefixes; measuring that residual on held-out gold prefixes would predict how much reward FaRMA leaves on the table.
- One-call-per-step scoring makes FaRMA's cost essentially independent of vocabulary size and top-$k$ width, so the speed advantage should widen on larger models and longer generations, a claim that could be tested by scaling the decoder while holding the reward model fixed.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes FaRMA, a reward-model architecture for reward-guided text generation (RGTG) that scores all possible next-token continuations in a single forward pass. The value model is trained with a Bradley-Terry loss on full-sequence preferences (Eq. 6) plus a squared max-consistency loss on prefixes of winning sequences (Eq. 7), alternated in Algorithm 1. The authors claim that previous RGTG value models (PARGS, CD) can prefer prefixes with suboptimal extensions (Theorems 1-2), and that FaRMA guarantees optimal-prefix preference (Theorem 3). Empirically, they report faster decoding than ARGS, PARGS, CD, and CARDS, with competitive reward scores on TL;DR, HH-RLHF, and UltraFeedback, along with GPT-4 win-rate and diversity evaluations. Code is provided.
Significance. If the empirical results hold, FaRMA is a practically useful contribution to RGTG: it reduces the number of reward-model calls per decoding step, cutting generation time by roughly 6x on TL;DR while matching or exceeding existing RGTG and offline-RLHF reward scores. The paper also ships reproducible code, detailed hyperparameters, and training-time comparisons. However, the paper's central theoretical claim, Theorem 3, is presented as the reason to prefer FaRMA over prior methods, and that claim is not established. The proof assumes the very property that training is supposed to deliver, and the training data for the constraint loss does not cover all prefixes over which the theorem quantifies. The empirical speed and quality results are credible, but the theoretical contribution needs substantial revision before the paper can be accepted.
major comments (3)
- [Section 4.2 / Theorem 3] The proof of Theorem 3 assumes, via the line "Since the loss in (7) ensures that the learned value function returns the reward of the best full sequence that extends a prefix," that Eq. (7) holds exactly for every prefix. But Algorithm 1 applies the constraint loss L(b) only to prefixes of winning sequences in D_max, as stated in Section 4.2: "we extract partial sequences from the winning sequences in the preference dataset and use them as training data." For any prefix y'_1:j that is not a prefix of a winning sequence, neither the BT loss (6) nor the constraint loss (7) directly constrains V(y'_1:j|x). The theorem quantifies over all y', so unconstrained prefixes can receive arbitrarily high values and inequality (9) can fail even if Eq. (7) were exactly satisfied on the training prefixes. The proof would need to show either that all prefixes relevant to decoding appear in D_max or that the constraint generalizes; neither is shown.
- [Algorithm 1 / Appendix B] The theorem's premise is also not linked to the optimization procedure. The alternating training runs only 5-7 iterations on each loss (Appendix B, Table 6) and uses bootstrap targets max_{y_{i+1}} V_theta(y_{1:i+1}|x). The paper provides no convergence analysis showing that a joint minimizer of (6) and (7) exists, that gradient descent reaches such a fixed point, or that the bootstrap targets are stable. Therefore the phrase "in the limit of infinite training data and a sufficiently expressive representation" in Theorem 3 does not justify the claim that the constraint holds pointwise; it merely restates the desired outcome as an assumption.
- [Theorem 3 proof] The step "Since the loss in (7) ensures that the learned value function returns the reward of the best full sequence that extends a prefix" is essentially the theorem's conclusion. The proof shows that if V satisfies the max-consistency property, then V prefers optimal extensions; this is true by definition and does not demonstrate that FaRMA's training obtains such a V. As a result, the theorem does not provide an independent guarantee, and the paper's abstract and Section 1 overstate the theoretical support for the method.
minor comments (6)
- [Algorithm 2] In Algorithm 2, line 3, the score is written as log(pi_ref(v|x, y_{1:i-1}) + beta * V_theta(v|x, y_{1:i-1})), which is inconsistent with Equation (1), where the log-probability and the value term are added before taking the softmax. The expression should be log pi_ref(v|x, y_{1:i-1}) + beta * V_theta(v|x, y_{1:i-1}).
- [Section 4.1] The sentence "In order to get the score of sequence x, y_{1:i} we feed the x, y_{1:i-1} into r_phi" refers to r_phi, but the model being described is the value function V_theta; please correct the notation.
- [Section 6] The text refers to "Appendix 12" when discussing the CARDS reward threshold; this should be Appendix G or Table 12.
- [Table 4 caption] The caption contains a duplicated word: "shows shows" should be "shows".
- [Figure 2] The GPT-4 win-rate results are only shown as plots; reporting the numerical win rates in a table would improve reproducibility and allow readers to verify the claimed competitive performance.
- [Theorems 1 and 2 / Abstract] Theorems 1 and 2 establish that PARGS and CD may prefer suboptimal prefixes under specific constructed datasets or base-model distributions. The abstract's wording "demonstrate that prior techniques prefer sub-optimal sequences" overstates this to a general claim; consider rephrasing to "can prefer" or "may prefer."
Circularity Check
Theorem 3's 'guarantee' reduces to the definition of the constraint: the proof assumes Eq. (7) is fully satisfied, which is exactly what training is supposed to establish.
-
self definitional
[Section 4.2, Theorem 3 proof (Eqs. 5, 7, 9)]
"Since the loss in (7) ensures that the learned value function returns the reward of the best full sequence that extends a prefix then V (y∗1:i|x) = r(y∗|x). Similarly, since y′1:j is any other prefix whose extensions do not lead to better full sequences, then V (y′1:j|x) ≤ r(y∗|x). This means that V (y′1:j|x) ≤ V (y∗1:i|x), which contradicts (10)."
The theorem's conclusion (9) is exactly the property defined by Eq. (5): a prefix's value equals the maximum value over its continuations. If that equality holds for every prefix, then any prefix of an optimal y* has value r(y*) and any other prefix has value at most r(y*), so (9) follows immediately. The proof does not derive Eq. (5) from the training procedure; it assumes the loss (7) 'ensures' the property. Algorithm 1 applies L(b) only to prefixes of winning sequences in D_max and runs 3-7 alternating steps (Appendix B), with a bootstrap target max_{yi+1} Vθ(y1:i+1|x). No existence or convergence proof is given for a joint minimizer of (6) and (7), and prefixes outside D_max are unconstrained. Thus the central theoretical guarantee is taken as the premise rather than derived.
-
other
[Section 4.2, Eq. (7) and Algorithm 1]
"L(b) = 1/2 [Vθ(y1:i|x) − max_{yi+1} Vθ(y1:i+1|x)]^2 ... For the constraint loss (7), we extract partial sequences from the winning sequences in the preference dataset and use them as training data."
The constraint loss has no independent supervised target: it aligns each prefix with the maximum over the same network's one-step continuations. A value function that is constant along every chain would make L(b) zero, so the optimal-expansion property must come from the full-sequence BT loss and from convergence of the bootstrap, neither of which is proved. The theorem treats the bootstrapped fixed point as already attained, and the universal quantification over all prefixes y'_{1:j} in (9) goes beyond the winning-sequence prefixes used to train L(b), so the proof's step 'whose extensions do not lead to better full sequences' is an assumption about unvisited inputs rather than a consequence of the training loss.
full rationale
The paper's empirical contributions—single-call scoring architecture, measured inference speedups, reward scores, and GPT-4 win rates—are externally evaluated and not circular. The critique of PARGS invokes Lemma 2 from the authors' prior work (Rashid et al., 2024), but that self-citation supports an analysis of a baseline, not the paper's central claim, so it is not load-bearing. The central theoretical claim, however, is circular in structure: Theorem 3 promises that FaRMA's learned value function scores optimal-expandable prefixes highest, and the proof's only substantive step is 'Since the loss in (7) ensures that the learned value function returns the reward of the best full sequence that extends a prefix.' That is precisely the property the loss is defined to enforce and precisely the fixed point whose attainment by Algorithm 1 is never proved. With no convergence or bounded-error analysis, the theorem reduces to a restatement of the optimization objective: if the Bellman-like consistency constraint holds everywhere, then the value function is the optimal continuation value and inequality (9) follows by definition. This makes the headline theoretical guarantee largely self-definitional, while leaving the empirical speed/quality results intact.
Assumptions & free parameters
free parameters (2)
- beta (reward scaling) =
1.5 (optimal on TL;DR; range 0.5-2.0 tested)
- top-k =
not reported in main text
assumptions (5)
- domain assumption Token-level MDP with deterministic transitions and no intermediate reward, so the Bellman equation reduces to Q*(s,a) = max_{a'} Q*(s',a') (Eq. 8).
- ad hoc to paper A sufficiently expressive value function and infinite training data allow L(b) to be driven to zero on all relevant prefixes.
- domain assumption The Bradley-Terry model captures pairwise preferences and the preference dataset is i.i.d. from the target reward distribution.
- domain assumption The full-sequence reward model used for evaluation is a reliable proxy for human preference.
- domain assumption Prefixes of winning responses in the preference dataset provide sufficient coverage of the state space for the constraint loss.
Cite this review
Pith. "Pith review of Towards Cost-Effective Reward Guided Text Generation." pith.science (2026). https://pith.science/paper/I4452XJD
@misc{pith2026250204517,
author = {Pith},
title = {Pith review of: Towards Cost-Effective Reward Guided Text Generation},
year = {2026},
howpublished = {\url{https://pith.science/paper/I4452XJD}},
note = {Machine review of arXiv:2502.04517}
}
read the original abstract
Reward-guided text generation (RGTG) has emerged as a viable alternative to offline reinforcement learning from human feedback (RLHF). RGTG methods can align baseline language models to human preferences without further training like in standard RLHF methods. However, they rely on a reward model to score each candidate token generated by the language model at inference, incurring significant test-time overhead. Additionally, the reward model is usually only trained to score full sequences, which can lead to sub-optimal choices for partial sequences. In this work, we present a novel reward model architecture that is trained, using a Bradley-Terry loss, to prefer the optimal expansion of a sequence with just a \emph{single call} to the reward model at each step of the generation process. That is, a score for all possible candidate tokens is generated simultaneously, leading to efficient inference. We theoretically analyze various RGTG reward models and demonstrate that prior techniques prefer sub-optimal sequences compared to our method during inference. Empirically, our reward model leads to significantly faster inference than other RGTG methods. It requires fewer calls to the reward model and performs competitively compared to previous RGTG and offline RLHF methods.
Figures
Forward citations
Cited by 1 Pith paper
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Safe Inference-Time Alignment via Lagrangian Reward Augmentation
Dualizing Safe RLHF yields a one-dimensional convex calibration of λ that defines a drop-in safety-aware reward for Best-of-N and token-level inference-time decoders.
Reference graph
Works this paper leans on
-
[1]
Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022
arXiv 2022
-
[2]
Bradley, R. A. and Terry, M. E. Rank analysis of incomplete block designs: The method of paired comparisons. Biometrika, 39 0 (3/4), 1952
work page 1952
-
[3]
Cao, M., Shu, L., Yu, L., Zhu, Y., Wichers, N., Liu, Y., and Meng, L. Beyond sparse rewards: Enhancing reinforcement learning with language model critique in text generation, 2024
work page 2024
-
[4]
PPL-MCTS : Constrained textual generation through discriminator-guided MCTS decoding
Chaffin, A., Claveau, V., and Kijak, E. PPL-MCTS : Constrained textual generation through discriminator-guided MCTS decoding. In NAACL, pp.\ 2953--2967, 2022
work page 2022
-
[5]
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P. Vicuna : An open-source chatbot impressing GPT-4 with 90\ URL https://lmsys.org/blog/2023-03-30-vicuna/
work page 2023
-
[6]
F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D. Deep reinforcement learning from human preferences. In NIPS, 2017
work page 2017
-
[7]
Plug and play language models: A simple approach to controlled text generation
Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., and Liu, R. Plug and play language models: A simple approach to controlled text generation. In ICLR, 2019
work page 2019
-
[8]
Deng, H. and Raffel, C. Reward-augmented decoding: Efficient controlled text generation with a unidirectional reward model. In EMNLP, 2023
work page 2023
Show all 44 references
-
[9]
RAFT : Reward ranked finetuning for generative foundation model alignment
Dong, H., Xiong, W., Goyal, D., Zhang, Y., Chow, W., Pan, R., Diao, S., Zhang, J., KaShun, S., and Zhang, T. RAFT : Reward ranked finetuning for generative foundation model alignment. TMLR, 2023
2023
-
[10]
Hierarchical neural story generation
Fan, A., Lewis, M., and Dauphin, Y. Hierarchical neural story generation. In ACL, 2018
2018
-
[11]
Y., Ding, N., Yao, G., He, B., Zhu, W., Ni, Y., Xie, G., Xie, R., Lin, Y., Liu, Z., and Sun, M
Ganqu Cui, L. Y., Ding, N., Yao, G., He, B., Zhu, W., Ni, Y., Xie, G., Xie, R., Lin, Y., Liu, Z., and Sun, M. Ultrafeedback: Boosting language models with scaled ai feedback. In ICML, 2024
2024
-
[12]
Value augmented sampling for language model alignment and personalization
Han, S., Shenfeld, I., Srivastava, A., Kim, Y., and Agrawal, P. Value augmented sampling for language model alignment and personalization. arXiv preprint arXiv:2405.06639, 2024
2024 arXiv
-
[13]
The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y. The curious case of neural text degeneration. In International Conference on Learning Representations, 2020
2020
-
[14]
Grace: Discriminator-guided chain-of-thought reasoning
Khalifa, M., Logeswaran, L., Lee, M., Lee, H., and Wang, L. Grace: Discriminator-guided chain-of-thought reasoning. In EMNLP, 2023
2023
-
[15]
Alignment as reward-guided search
Khanov, M., Burapacheep, J., and Li, Y. Alignment as reward-guided search. In ICLR, 2024
2024
-
[16]
D., McCann, B., Keskar, N
Krause, B., Gotmare, A. D., McCann, B., Keskar, N. S., Joty, S., Socher, R., and Rajani, N. F. GeDi : Generative discriminator guided sequence generation. In EMNLP, 2021
2021
-
[17]
Rankgen: Improving text generation with large ranking models
Krishna, K., Chang, Y., Wieting, J., and Iyyer, M. Rankgen: Improving text generation with large ranking models. In EMNLP, 2022
2022
-
[18]
M., and Abbeel, P
Lee, K., Smith, L. M., and Abbeel, P. PEBBLE : Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training. In ICML, 2021
2021
-
[19]
Cascade reward sampling for efficient decoding-time alignment
Li, B., Wang, Y., Grama, A., and Zhang, R. Cascade reward sampling for efficient decoding-time alignment. arXiv preprint arXiv:2406.16306, 2024
2024 arXiv
-
[20]
Making language models better reasoners with step-aware verifier
Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., and Chen, W. Making language models better reasoners with step-aware verifier. In ACL, 2023
2023
-
[21]
Let's verify step by step
Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K. Let's verify step by step. In ICLR, 2023
2023
-
[22]
Chain of hindsight aligns language models with feedback
Liu, H., Sferrazza, C., and Abbeel, P. Chain of hindsight aligns language models with feedback. In ICLR, 2023 a
2023
-
[23]
Attribute controlled dialogue prompting
Liu, R., Rashid, A., Kobyzev, I., Rezagholizadeh, M., and Poupart, P. Attribute controlled dialogue prompting. In ACL, 2023 b
2023
-
[24]
Controlled decoding from language models
Mudgal, S., Lee, J., Ganapathy, H., Li, Y., Wang, T., Huang, Y., Chen, Z., Cheng, H.-T., Collins, M., Strohman, T., et al. Controlled decoding from language models. In ICML, 2024
2024
-
[25]
Webgpt: Browser-assisted question-answering with human feedback
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021
2021 arXiv
-
[26]
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. Training language models to follow instructions with human feedback. In NeurIPS, 2022
2022
-
[27]
D., Ermon, S., and Finn, C
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C. Direct preference optimization: Your language model is secretly a reward model. In NeurIPS, 2023
2023
-
[28]
From r to Q^* : Your language model is secretly a Q -function
Rafailov, R., Hejna, J., Park, R., and Finn, C. From r to Q^* : Your language model is secretly a Q -function. arXiv preprint arXiv:2404.12358, 2024
2024 arXiv
-
[29]
A critical look at tokenwise reward-guided text generation
Rashid, A., Wu, R., Grosse, J., Kristiadi, A., and Poupart, P. A critical look at tokenwise reward-guided text generation. arXiv preprint arXiv:2406.07780, 2024
2024
-
[30]
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017
2017 arXiv
-
[31]
Offline RL for natural language generation with implicit language Q learning
Snell, C., Kostrikov, I., Su, Y., Yang, M., and Levine, S. Offline RL for natural language generation with implicit language Q learning. arXiv preprint arXiv:2206.11871, 2022
2022 arXiv
-
[32]
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33: 0 3008--3021, 2020 a
2020
-
[33]
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F. Learning to summarize with human feedback. In NeurIPS, 2020 b
2020
-
[34]
Solving math word problems with process-and outcome-based feedback
Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., and Higgins, I. Solving math word problems with process-and outcome-based feedback. arXiv preprint arXiv:2211.14275, 2022
2022 arXiv
-
[35]
Tl; dr: Mining reddit to learn automatic summarization
V \"o lske, M., Potthast, M., Syed, S., and Stein, B. Tl; dr: Mining reddit to learn automatic summarization. In Proceedings of the Workshop on New Frontiers in Summarization, pp.\ 59--63, 2017
2017
-
[36]
W., Lester, B., Du, N., Dai, A
Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V. Finetuned language models are zero-shot learners. In ICLR, 2021
2021
-
[37]
Naturalprover: Grounded mathematical proof generation with language models
Welleck, S., Liu, J., Lu, X., Hajishirzi, H., and Choi, Y. Naturalprover: Grounded mathematical proof generation with language models. In NeurIPS, 2022
2022
-
[38]
A., Ostendorf, M., and Hajishirzi, H
Wu, Z., Hu, Y., Shi, W., Dziri, N., Suhr, A., Ammanabrolu, P., Smith, N. A., Ostendorf, M., and Hajishirzi, H. Fine-grained human feedback gives better rewards for language model training. NeurIPS, 2023
2023
-
[39]
and Klein, D
Yang, K. and Klein, D. Fudge: Controlled text generation with future discriminators. In NAACL, 2021
2021
-
[40]
Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K. Tree of thoughts: Deliberate problem solving with large language models. In NeurIPS, 2023
2023
-
[41]
Probabilistic inference in language models via twisted sequential M onte C arlo
Zhao, S., Brekelmans, R., Makhzani, A., and Grosse, R. Probabilistic inference in language models via twisted sequential M onte C arlo. In ICML, 2024
2024
-
[42]
Judging LLM -as-a-judge with MT -bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al. Judging LLM -as-a-judge with MT -bench and chatbot arena. In NeurIPS, 2023
2023
-
[44]
M., Stiennon, N., Wu, J., Brown, T
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019 b
1909 arXiv
-
[45]
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 gl...
Reviewed August 8, 2026 · model on record in the stance chip above.
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