REVIEW 4 major objections 6 minor 33 references
Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time
T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read Satisficing beats weighted rewards: SITAlign decodes LLMs to a primary goal with thresholds on secondary ones.
desk verdict A worthwhile threshold-based decoding idea that deserves a referee, but the headline empirical claim is not yet backed: constraint adherence is asserted from win-tie rates rather than verified in reward space. 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 mechanism is the Lagrangian of a constrained controlled-decoding Markov decision process. With a strongly convex KL regularizer, maximizing the Lagrangian over token distributions gives the closed form $\pi(z|s_t) \propto \pi_{\mathrm{BL}}(z|s_t)\exp\left(\frac{1}{\beta_1}\sum_{i=1}^{N}\lambda_i \mathrm{TQ}^*_i(s_t,z)\right)$, and minimizing over $\lambda$—approximated by a quadratic expansion around $\lambda=0$—fixes the multiplier vector. Here $\mathrm{TQ}^*$ is the estimated optimal action-value: the expected reward of continuations sampled from a baseline trajectory policy. The formula explains the reported trade-off: larger $\lambda_i$ shifts probability toward tokens whose continuations score high on reward $i$, which is exactly how the threshold is enforced.
What would settle it
Test on a held-out prompt set by decoding with SITAlign and recording the realized secondary-reward (for example, harmlessness) rate. If the observed violation rate systematically exceeds the rate implied by the chosen threshold, or if a brute-force rollout-based verification reverses TQ*'s ranking of candidate continuations, then the TQ* approximation is not close enough to the true optimal action-value and the claimed constraint guarantee does not hold.
Extended reading notes
Core claim
SITAlign's core claim is that at each decoding step the optimal token distribution is a KL-regularized reweighting of a baseline policy by a linear combination of action-value estimates, where the first reward's coefficient is fixed to 1 and the other coefficients (Lagrange multipliers) are chosen so that secondary expected rewards clear user-set thresholds. This turns alignment from a global training objective into a per-token constrained optimization problem solved in closed form. The paper claims this satisficing decoding outperforms the state-of-the-art weighted multi-objective decoder in baseline reward win-tie rate while respecting the threshold on the target reward, in all three evaluated task setups.
Load-bearing premise
The method depends on the assumption that the cheap estimate TQ*, obtained by averaging rewards over continuations from a fixed baseline policy, is close to the true optimal action-value for every reward at every decoding step; if those estimates are biased, the decoded policy need not actually respect the stated thresholds.
Editorial extensions
If this is right
- If SITAlign is correct, thresholds become user-tunable dials: raising a secondary threshold trades down the primary reward, and lowering it restores primary performance.
- Decoding-time satisficing removes the need to retrain or finetune a model for each new threshold or reward combination.
- The framework generalizes prior controlled decoding: when no constraint is active, the multipliers vanish and it reduces to unconstrained Transfer Q* decoding.
- The approach gives practitioners a principled way to enforce safety constraints without sacrificing helpfulness on every prompt.
- The paper's ablation results imply that beyond a satisficing threshold, further gains on the secondary reward plateau, so threshold-based control can avoid wasteful overmaximization.
Reading between the lines
- Editorial extension: the satisficing view suggests reward models only need to be well-calibrated near the threshold, not across the entire reward range, which could make threshold selection cheaper and more robust than full preference tuning.
- Editorial extension: the same constrained-decoding trick should apply to verifiable constraints such as toxicity scores, length limits, or faithfulness metrics, not just learned reward models; the paper does not test this.
- Editorial extension: a testable refinement is per-prompt or per-user threshold calibration, since a single global threshold may over-constrain easy prompts and under-constrain hard ones; the paper's ablations vary the threshold only globally.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes SITAlign, an inference-time constrained decoding framework that maximizes a primary reward while enforcing lower-bound thresholds on secondary rewards. The method is derived from a Lagrangian dual formulation: the primal is a Boltzmann-style policy (Eq. 5), and the dual variables are obtained from a quadratic approximation of the dual objective around λ=0 (Eq. 7). Action-value functions are estimated by Transfer Q* (TQ*, Eq. 8) using fixed baseline policies. The paper provides suboptimality bounds for the primal and dual approximations (Theorems 5.1 and 5.2) and evaluates the method on three benchmarks (PKU-SafeRLHF, Anthropic-HH, Summarize-from-Feedback) using GPT-4 win-tie rates, reporting improvements over the MOD baseline, including a 22.3% helpfulness win-tie advantage on Evaluation-1.
Significance. If the central claim holds, the satisficing formulation is a conceptually interesting alternative to weighted multi-objective decoding, and the inference-time design avoids per-user retraining. The paper's strengths include a clear problem formulation, an explicit Lagrangian derivation, and the use of reproducible public resources. However, the theoretical guarantees as stated do not establish that the constrained problem in Eq. (2) is actually solved: the dual update rests on an unvalidated quadratic approximation, the bounds contain unquantified constants, and the theorem proof is imported from prior work. The empirical evaluation likewise verifies constraint adherence only through the same GPT-4 win-tie metric used to calibrate the thresholds, and it does not report distributions of the actual secondary rewards. The significance is therefore conditional on closing these gaps.
major comments (4)
- [Section 4, Eq. (7)] The closed-form dual update is derived from a second-order Taylor expansion of Z_λ around λ=0, but the paper provides no bound on the approximation error in terms of problem data. Consequently λ*_Alg may be far from λ*, and Theorem 5.2's bound on Sub-Gap2 depends on unquantified constants L_log, L_Z, and Λ, where Λ is only expressed via an abstract Slater's-rule bound (Appendix B, Step 4) that is never evaluated. The text therefore does not guarantee that the output of Algorithm 1 satisfies the threshold constraints E_{z∼π}[TQ*_i] ≥ β_i. A central claim of the paper is that SITAlign "adheres to the threshold" (Abstract); that claim requires either a formal bound on constraint violation or an explicit numerical check of feasibility.
- [Section 5, Theorem 5.1] The proof of Theorem 5.1 is not self-contained: the paper states "The proof follows from Appendix E in (Chakraborty et al., 2024)" and only sketches modifications. More importantly, the theorem bounds the Lagrangian gap Sub-Gap1, not the feasibility gap. A policy can have a small Lagrangian gap while violating the constraints by a wide margin, so the theorem does not support the assertion that SITAlign "solves" the constrained decoding problem (Eq. 2). The authors should provide a self-contained proof and, crucially, a bound or empirical certificate of constraint satisfaction.
- [Section 6.2 and 6.3] The empirical evaluation does not verify the constraint in the space where it is imposed. Algorithm 1 enforces thresholds β_i on E[TQ*_i] (Eqs. 2 and 8), but Section 6 reports only GPT-4 win-tie rates. Section 6.3 states that "setting the threshold β = −12 and β = −0.5 achieves a 50% win-tie rate" in Evaluations 1 and 2, which means the threshold is calibrated to produce the very win-tie metric that is later reported as evidence of constraint adherence. No table or figure shows the realized distribution of the actual harmlessness (or humor/faithfulness) reward on the final responses relative to β_2. Thus the headline 22.3% helpfulness improvement does not establish that SITAlign satisfies the stated thresholds; a policy whose expected TQ*_2 is far below β_2 could still achieve a 50% win-tie rate against a weak baseline. The authors should report the actual reward-model scores of the generated responses and verify that the constraints hold in expectation.
- [Section 4, Eq. (8) and Algorithm 1] The theory in Eqs. (5)–(6) is stated for the optimal action-value functions Q^{π*,λ}_i, but Algorithm 1 substitutes TQ*_i estimated from a fixed trajectory-level baseline ρ_BL. The paper gives no quantitative bound on the bias ||TQ*_i − Q*_i|| or on how this substitution propagates to the primal-dual solution. Without such a bound, solving the modified problem (15) does not entail solving the original problem (2). The authors should either provide a bias bound under stated assumptions or compare TQ* against a Monte Carlo estimate of Q* on the evaluation tasks to show the approximation is reliable.
minor comments (6)
- [Eq. (2)] The notation is inconsistent: the expectation is written as E_{a∼π(·|st)} but the integration variable in the argument is z. It should be E_{z∼π(·|st)} throughout.
- [Algorithm 1] The input list includes the "decoding alignment parameter α" and the KL coefficient β_1, but α is never used in the loop. Specify whether α enters through the baseline policies ρ_BL_i or is otherwise omitted, and clarify the relationship between α and β_1.
- [Section 6.1] The text says "a greedy-based sampling method is employed" for baselines, while Algorithm 1 samples from the top-k set. Clarify whether SITAlign also uses greedy token selection after restricting to the top-k set, and state this explicitly in the experimental setup.
- [Section 6.2, Qualitative Evaluation] The sentence "We further corroborate this through a qualitative analysis presented in Figure 6.1" refers to a figure that does not exist in the text; the example appears to be a block quote rather than a numbered figure. Please renumber or relabel.
- [Theorem 5.1 and Appendix B] The notation switches between D_KL[π*_Alg(·|x)||ρ_sft(·|x)] in the theorem and D_KL[ρ*_Alg(·|x)||ρ_sft(·|x)] in the proof, and the chain from the token-level to trajectory-level divergence is not written out. Make the notation consistent and spell out the relationship.
- [References] There are several presentation issues in the references: "ly usable llms" in the MPT entry, "Dai et al." without a year, and the repeated citation of Chakraborty et al. as both the TQ* method and the source of the deferred proof. Clean these up.
Circularity Check
Constraint adherence is calibrated to the same GPT-4 win-tie metric used to report success, and the main suboptimality theorem is imported from the authors' own TQ* paper; the primary-reward comparison against MOD remains independent.
-
fitted input called prediction
[Section 6.2 (Evaluation Methodology and Results) and Section 6.3 (Ablations on threshold constraint)]
"Selection of threshold. ... One effective approach is to leverage GPT-4 win rates to estimate a reasonable threshold a priori. ... For experimental consistency, we have standardized the constraint criteria to a 50% win-tie rate on the target reward. This stems from the experimental evidence in Figure 1, which shows that the reward scores are highly correlated with GPT-4 evaluation. ... Note that, setting the threshold β = −12 and β = −0.5 achieves a 50% win-tie rate in Evaluation-1 and 2 respectively."
The paper's constrained optimization problem (Eq. 2) imposes thresholds β2 on the harmlessness action-value Q*2, but the experiments verify 'adhering to the threshold on harmlessness' only as a 50% GPT-4 win-tie rate on the target reward. The threshold itself is selected or confirmed using GPT-4 win rates, and Section 6.3 states that β = −12 achieves exactly the 50% win-tie rate used as the success criterion. Thus the constraint-adherence result is not an independent check in the reward space of Eq. (2); it is a fitted target: the evaluation metric is the same win-tie rate used to set/verify the threshold, and no distribution of the final harmlessness reward relative to β2 = −12 is reported.
-
self citation load bearing
[Appendix B, proof of Theorem B.1 (restating Theorem 5.1); Section 5.1]
"Proof. The proof follows from Appendix E in (Chakraborty et al., 2024). In that paper, only one reward function is considered. On the other hand, in our work, after forming the Lagrangian, we obtain a linear combination of the rewards PN i=1 λ∗ i ri."
The paper advertises 'theoretical insights' and derives suboptimality bounds in Theorem 5.1, but the proof is not provided here; it is explicitly delegated to Chakraborty et al. (2024). That cited work shares multiple authors with this paper (Chakraborty, Ghosal, Manocha, and Bedi), so the central theoretical guarantee is load-bearing on a self-citation rather than on an independent, machine-checked, or externally verified derivation. The only multi-reward adaptation offered is replacing a single reward r with the linear combination Σ λ_i r_i, which does not re-derive the bound or its assumptions. This makes the paper's theoretical justification for SITAlign reduce to the authors' own prior result.
full rationale
The primary-utility comparison in the paper is genuinely independent: SITAlign's 22.3% helpfulness win-tie improvement over MOD on PKU-SafeRLHF is an external benchmark against a different method, and GPT-4 win-tie evaluation is a standard, non-circular metric for relative response quality. That part of the empirical claim is not fitted to the result and does not reduce to the method's inputs. However, the abstract's paired claim of 'adhering to the threshold on harmlessness' has a circular component. The paper standardizes the constraint criterion to a 50% GPT-4 win-tie rate on the target reward, and Section 6.3 reports that the threshold β = −12 is the value that achieves a 50% win-tie rate. The success metric and the threshold-calibration signal are therefore the same measurement; the paper never reports the realized harmlessness reward distribution against the β2 = −12 threshold in the reward space where Eq. (2) imposes the constraint. Consequently, the constraint-satisfaction result is calibrated rather than independently verified. Additionally, the main theoretical result is imported wholesale from the authors' own TQ* paper, with only a linear-combination adaptation, making the formal suboptimality guarantee rest on an unverified self-citation. These issues warrant a score of 6: one component of the central claim (constraint adherence) reduces by construction, and the theoretical bound is self-citation load-bearing, but the core primary-reward comparison against MOD retains independent content. The unquantified TQ*-for-Q* substitution in Eq. (8) is a correctness risk rather than a circularity, because it is an approximation with no claimed equivalence to the exact Q*.
Assumptions & free parameters
free parameters (6)
- Threshold beta_2 (harmlessness, Evaluation-1) =
-12
- Threshold beta_2 (humor, Evaluation-2) =
-0.5
- Threshold beta_2 (faithfulness, Evaluation-3) =
not stated
- KL coefficient beta_1 =
not stated
- Decoding alignment parameter alpha =
1
- Top-k token set size k =
10
assumptions (4)
- domain assumption Transfer Q* (Eq. 8) is a sufficiently accurate estimate of the true optimal action-value Q* for all rewards at all decoding steps.
- domain assumption Strong duality holds and Slater's condition is satisfied for the constrained decoding problem at every state.
- domain assumption Reward-model scores and GPT-4 win-tie rates are valid proxies for human acceptability, and a single threshold separates acceptable from unacceptable responses.
- domain assumption All rewards are bounded with 0 <= r_i <= r_max as used in Theorem 5.1.
Cite this review
Pith. "Pith review of Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time." pith.science (2026). https://pith.science/paper/3BXTBKB4
@misc{pith2026250523729,
author = {Pith},
title = {Pith review of: Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time},
year = {2026},
howpublished = {\url{https://pith.science/paper/3BXTBKB4}},
note = {Machine review of arXiv:2505.23729}
}
read the original abstract
Aligning large language models with humans is challenging due to the inherently multifaceted nature of preference feedback. While existing approaches typically frame this as a multi-objective optimization problem, they often overlook how humans actually make decisions. Research on bounded rationality suggests that human decision making follows satisficing strategies-optimizing primary objectives while ensuring others meet acceptable thresholds. To bridge this gap and operationalize the notion of satisficing alignment, we propose SITAlign: an inference time framework that addresses the multifaceted nature of alignment by maximizing a primary objective while satisfying threshold-based constraints on secondary criteria. We provide theoretical insights by deriving sub-optimality bounds of our satisficing based inference alignment approach. We empirically validate SITAlign's performance through extensive experimentation on multiple benchmarks. For instance, on the PKU-SafeRLHF dataset with the primary objective of maximizing helpfulness while ensuring a threshold on harmlessness, SITAlign outperforms the state-of-the-art multi objective decoding strategy by a margin of 22.3% in terms of GPT-4 win-tie rate for helpfulness reward while adhering to the threshold on harmlessness.
Figures
Reference graph
Works this paper leans on
-
[1]
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. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023
arXiv 2023
-
[2]
A general language assistant as a laboratory for alignment
Askell, A., Bai, Y., Chen, A., Drain, D., Ganguli, D., Henighan, T., Jones, A., Joseph, N., Mann, B., DasSarma, N., et al. A general language assistant as a laboratory for alignment. arXiv preprint arXiv:2112.00861, 2021
arXiv 2021
-
[4]
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 b
arXiv 2022
-
[5]
Bradley, R. A. and Terry, M. E. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, 39 0 (3/4): 0 324--345, 1952
1952
-
[6]
S., Yin, M., Manocha, D., Wang, M., Bedi, A
Chakraborty, S., Ghosal, S. S., Yin, M., Manocha, D., Wang, M., Bedi, A. S., and Huang, F. Transfer q star: Principled decoding for llm alignment. arXiv preprint arXiv:2405.20495, 2024
arXiv 2024
-
[7]
Self-play fine-tuning converts weak language models to strong language models
Chen, Z., Deng, Y., Yuan, H., Ji, K., and Gu, Q. Self-play fine-tuning converts weak language models to strong language models. arXiv preprint arXiv:2401.01335, 2024
arXiv 2024
-
[8]
Safe rlhf: Safe reinforcement learning from human feedback
Dai, J., Pan, X., Sun, R., Ji, J., Xu, X., Liu, M., Wang, Y., and Yang, Y. Safe rlhf: Safe reinforcement learning from human feedback. In The Twelfth International Conference on Learning Representations
-
[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., Shum, K., and Zhang, T. Raft: Reward ranked finetuning for generative foundation model alignment. arXiv preprint arXiv:2304.06767, 2023
arXiv 2023
Show all 33 references
-
[10]
Llmcarbon: Modeling the end-to-end carbon footprint of large language models
Faiz, A., Kaneda, S., Wang, R., Osi, R., Sharma, P., Chen, F., and Jiang, L. Llmcarbon: Modeling the end-to-end carbon footprint of large language models. arXiv preprint arXiv:2309.14393, 2023
2023 arXiv
-
[11]
Improving alignment of dialogue agents via targeted human judgements
Glaese, A., McAleese, N., Tr e bacz, M., Aslanides, J., Firoiu, V., Ewalds, T., Rauh, M., Weidinger, L., Chadwick, M., Thacker, P., et al. Improving alignment of dialogue agents via targeted human judgements. arXiv preprint arXiv:2209.14375, 2022
2022 arXiv
-
[12]
Y., Sengupta, S., Bonadiman, D., Lai, Y.-a., Gupta, A., Pappas, N., Mansour, S., Kirchhoff, K., and Roth, D
Huang, J. Y., Sengupta, S., Bonadiman, D., Lai, Y.-a., Gupta, A., Pappas, N., Mansour, S., Kirchhoff, K., and Roth, D. Deal: Decoding-time alignment for large language models. arXiv preprint arXiv:2402.06147, 2024 a
2024
-
[13]
One-shot safety alignment for large language models via optimal dualization
Huang, X., Li, S., Dobriban, E., Bastani, O., Hassani, H., and Ding, D. One-shot safety alignment for large language models via optimal dualization. arXiv preprint arXiv:2405.19544, 2024 b
2024 arXiv
-
[14]
Y., Wang, Y., Hessel, J., Zettlemoyer, L., Hajishirzi, H., Choi, Y., and Ammanabrolu, P
Jang, J., Kim, S., Lin, B. Y., Wang, Y., Hessel, J., Zettlemoyer, L., Hajishirzi, H., Choi, Y., and Ammanabrolu, P. Personalized soups: Personalized large language model alignment via post-hoc parameter merging. arXiv preprint arXiv:2310.11564, 2023
-
[15]
Pku-saferlhf: Towards multi-level safety alignment for llms with human preference
Ji, J., Hong, D., Zhang, B., Chen, B., Dai, J., Zheng, B., Qiu, T., Li, B., and Yang, Y. Pku-saferlhf: Towards multi-level safety alignment for llms with human preference. arXiv preprint arXiv:2406.15513, 2024
2024 arXiv
-
[16]
Args: Alignment as reward-guided search
Khanov, M., Burapacheep, J., and Li, Y. Args: Alignment as reward-guided search. arXiv preprint arXiv:2402.01694, 2024
2024 arXiv
-
[17]
Chain of hindsight aligns language models with feedback
Liu, H., Sferrazza, C., and Abbeel, P. Chain of hindsight aligns language models with feedback. arXiv preprint arXiv:2302.02676, 2023
2023 arXiv
-
[18]
L., Daly, R
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp.\ 142--150, Portland, Oregon, US...
2011
-
[19]
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. arXiv preprint arXiv:2310.17022, 2023
2023 arXiv
-
[20]
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
-
[21]
and Ozdaglar, A
Nedi \'c , A. and Ozdaglar, A. Approximate primal solutions and rate analysis for dual subgradient methods. SIAM Journal on Optimization, 19 0 (4): 0 1757--1780, 2009
2009
-
[22]
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. Advances in neural information processing systems, 35: 0 27730--27744, 2022
2022
-
[23]
Puterman, M. L. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons, 2014
2014
-
[24]
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. Advances in Neural Information Processing Systems, 36, 2024 a
2024
-
[25]
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. Advances in Neural Information Processing Systems, 36, 2024 b
2024
-
[26]
A., and Du, S
Shi, R., Chen, Y., Hu, Y., Liu, A., Hajishirzi, H., Smith, N. A., and Du, S. S. Decoding-time language model alignment with multiple objectives. The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024
2024
-
[27]
Simon, H. A. Rational choice and the structure of the environment. Psychological review, 63 0 (2): 0 129, 1956
1956
-
[28]
S., Tang, X., and Bogunovic, I
Son, S., Bankes, W., Yoon, S., Ramesh, S. S., Tang, X., and Bogunovic, I. Robust multi-objective controlled decoding of large language models. arXiv preprint arXiv:2503.08796, 2025
2025
-
[29]
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
-
[30]
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 b
2020
-
[31]
Team, M. N. Introducing mpt-7b: A new standard for open-source, ly usable llms, 2023. URL www.mosaicml.com/blog/mpt-7b
2023
-
[32]
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023
2023 arXiv
-
[33]
M., and Wolf, T
Tunstall, L., Beeching, E., Lambert, N., Rajani, N., Rasul, K., Belkada, Y., Huang, S., von Werra, L., Fourrier, C., Habib, N., Sarrazin, N., Sanseviero, O., Rush, A. M., and Wolf, T. Zephyr: Direct distillation of lm alignment, 2023
2023
-
[34]
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 7, 2026 · model on record in the stance chip above.
Discussion (0). Sign in to comment.