Papers › PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and...

PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling

13 Feb 2024arXiv:2402.08702archive 2025-07-28

Yongchao Chen, Jacob Arkin, Yilun Hao, Yang Zhang, Nicholas Roy, Chuchu Fan

Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task. LLMs have been successfully used to help find and improve prompt candidates for single-step tasks. However, realistic tasks for agents are multi-step and introduce new challenges: (1) Prompt content is likely to be more extensive and complex, making it more difficult for LLMs to analyze errors, (2) the impact of an individual step is difficult to evaluate, and (3) different people may have varied preferences about task execution. While humans struggle to optimize prompts, they are good at providing feedback about LLM outputs; we therefore introduce a new LLM-driven discrete prompt optimization framework PRompt Optimization in Multi-Step Tasks (PROMST) that incorporates human-designed feedback rules to automatically offer direct suggestions for improvement. We also use an extra learned heuristic model that predicts prompt performance to efficiently sample from prompt candidates. This approach significantly outperforms both human-engineered prompts and several other prompt optimization methods across 11 representative multi-step tasks (an average 10.6\%-29.3\% improvement to current best methods on five LLMs respectively). We believe our work can serve as a benchmark for automatic prompt optimization for LLM-driven multi-step tasks. Datasets and Codes are available at https://github.com/yongchao98/PROMST. Project Page is available at https://yongchao98.github.io/MIT-REALM-PROMST.

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="2402.08702")

Code

Syntology Ran 9 of 12 code samples harvested from 1 repository linked to this paper; 3 have no recorded run. Of those that ran: 9 ran with no contract checked.

By repository: official repository: 12 samples from 1 repository, 9 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

yongchao98/promst officialmentioned in papermentioned 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

12 samples harvested; 9 ran; 0 honoured the contract we drafted; 3 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.

9ran
3unverified

Licence: 0 of the 12 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 yongchao98/promst. “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.

action_from_response yongchao98/promst/BoxNet1/env1_create.py official repository ran MIT (permissive) · 7436a57078e7696c · report
corner_position yongchao98/promst/BoxNet2/env2_create.py official repository ran fingerprinted MIT (permissive) · f697b88d2cfd7249 · report
error_string_func_APO yongchao98/promst/BoxNet1/prompt_env1.py official repository ran MIT (permissive) · c42105f069a2cdbd · report
judge_move_box2pos_box2target_func yongchao98/promst/BoxNet2/env2_create.py official repository ran MIT (permissive) · 53a8de84b6bc929e · report
prompt_to_promptLLM_func_total_APE yongchao98/promst/BoxNet1/prompt_env1.py official repository ran fingerprinted MIT (permissive) · d45869657d4415b9 · report
read_file_content yongchao98/promst/BlocksWorld/env7-blocksworld-list-score.py official repository ran MIT (permissive) · 6c1cdb58b0bb57ab · report
state_update_func yongchao98/promst/BoxNet1/env1_create.py official repository ran MIT (permissive) · cebcc1ecc15d62ae · report
state_update_func yongchao98/promst/BoxNet2/env2_create.py official repository ran MIT (permissive) · a683c02bc4f7bac7 · report
surround_index_func yongchao98/promst/BoxNet1/env1_create.py official repository ran MIT (permissive) · 6a024a82611e53a1 · report
GPT_response yongchao98/promst/BoxNet1/LLM.py official repository unverified MIT (permissive) · 7f59d2db424b7b8d · report
score_in_training_set yongchao98/promst/BoxNet1/env1_func.py official repository unverified MIT (permissive) · 02b8bef5328aa45d · report
total_score_in_training_set yongchao98/promst/BoxNet1/env1_func.py official repository unverified MIT (permissive) · c87152fe97b7d199 · report

Tasks

Language ModellingLarge Language Model

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

ALIGNAbsolute Position EncodingsAdamAttentionAttention DropoutBPECosine AnnealingDense ConnectionsDropoutGPT-3GPT-4Label SmoothingLayer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformerWeight Decay

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