Papers › KTO: Model Alignment as Prospect Theoretic Optimization

KTO: Model Alignment as Prospect Theoretic Optimization

2 Feb 2024arXiv:2402.01306archive 2025-07-28

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, Douwe Kiela

Kahneman & Tversky's prospect theory tells us that humans perceive random variables in a biased but well-defined manner (1992); for example, humans are famously loss-averse. We show that objectives for aligning LLMs with human feedback implicitly incorporate many of these biases -- the success of these objectives (e.g., DPO) over cross-entropy minimization can partly be ascribed to them belonging to a family of loss functions that we call human-aware losses (HALOs). However, the utility functions these methods attribute to humans still differ from those in the prospect theory literature. Using a Kahneman-Tversky model of human utility, we propose a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, as current methods do. We call this approach KTO, and it matches or exceeds the performance of preference-based methods at scales from 1B to 30B, despite only learning from a binary signal of whether an output is desirable. More broadly, our work suggests that there is no one HALO that is universally superior; the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2402.01306")

Code

Syntology Ran 5 of 11 code samples harvested from 2 repositories linked to this paper; 6 have no recorded run. Of those that ran: 5 ran with no contract checked.

By repository: official repository: 9 samples from 1 repository, 5 ran; community: 2 samples from 1 repository, 0 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

contextualai/halos officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
openbmb/eurus mentioned on GitHubpytorchApache-2.0 report
reml-group/deliberation-on-priors mentioned on GitHubpytorch report
tidedra/vl-rlhf mentioned on GitHubpytorchApache-2.0 report
uppaal/detox-edit mentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

11 samples harvested; 5 ran; 0 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

5ran
6unverified

Licence: 2 of the 11 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

convert_to_binary_feedback contextualai/halos/train/label.py official repository ran Apache-2.0 (permissive) · 65cbf1ea1a7a5e74 · report
get_api_completion contextualai/halos/train/utils.py official repository ran Apache-2.0 (permissive) · 9257a83ca14f9dd7 · report
get_feedback contextualai/halos/train/data.py official repository ran Apache-2.0 (permissive) · 1ed0ab0ac3d9655a · report
pad_to_length contextualai/halos/train/utils.py official repository ran Apache-2.0 (permissive) · 38bf6f7230fcc687 · report
process_samples_with_api contextualai/halos/train/label.py official repository ran Apache-2.0 (permissive) · 500478230027c795 · report
get_alpacaeval contextualai/halos/train/data.py official repository unverified Apache-2.0 (permissive) · c54cf81b72eecb67 · report
get_base_model_state_dict_from_peft contextualai/halos/train/utils.py official repository unverified Apache-2.0 (permissive) · 124288769111d62f · report
get_sampled_data contextualai/halos/train/data.py official repository unverified Apache-2.0 (permissive) · 238fc17a8a274cea · report
process_batch_with_reward_model contextualai/halos/train/label.py official repository unverified Apache-2.0 (permissive) · 9bf64659a7cf741d · report
parse_args jacklishufan/diffusion-kto/train_kto_sd_v1.5.py community unverified no licence file found · pointer only · 20f9d022f6c9a501 · report
tokenize_captions jacklishufan/diffusion-kto/train_kto_sd_v1.5.py community unverified no licence file found · pointer only · 28cae3111ef3900a · report

Tasks

Attributemodel

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections