UserRepGen-4B is a fine-tuned Qwen3.5-4B language model that generates a structured, natural-language user representation from a multi-turn user-assistant conversation. Each representation contains a task description and a behavior description for a user, and is intended to condition a separate LLM-based user simulator.
Unlike UserLM-8b and UserLM-2.0, which generate user utterances, UserRepGen-4B generates representations, not simulated user turns. It does not itself execute the user's task or serve as the simulator.
The model was trained with online supervised fine-tuning through hindsight distillation, using feedback obtained by comparing simulated and original user behavior. At inference time, it generates a representation directly from the source conversation without requiring simulation rollouts or hindsight feedback.
Generates structured task and user-behavior descriptions from multi-turn user-assistant conversations.
Captures conversational behavior including communication style, request formulation, incremental constraint disclosure, response patterns, feedback, context provision, engagement, and termination behavior.
Produces representations that improve conversation-reconstruction fidelity when used with an unseen LLM-based simulation environment.
Captures some user-specific behavioral patterns that transfer across conversations and unseen tasks.
Produces representations that can help simulated populations better preserve the behavioral diversity of source conversations.
Produces behavior descriptions that improve same-user conversation matching in the accompanying research's re-identification evaluation. This capability also presents a privacy risk; representations are not anonymized data.
The following example uses Hugging Face Transformers. The conversation is a newly written, simplified example inspired by an Excel-help conversation in WildChat; it is not an original user transcript.
import json
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForImageTextToText, AutoTokenizer
model_id = "microsoft/UserRepGen-4B"
device = "cuda" if torch.cuda.is_available() else "cpu"
if device == "cpu":
torch.set_num_threads(8)
conversation = [
{"role": "user", "content": "I need an Excel formula to count dates in A2:A100 before today plus B1."},
{"role": "assistant", "content": 'Try =COUNTIF(A2:A100,"<"&TODAY()+B1).'},
{"role": "user", "content": "That didn't work. The dates come from another formula."},
{"role": "assistant", "content": "Are those results actual Excel dates, or text that looks like dates?"},
{"role": "user", "content": "They're text. Some cells are empty too."},
{"role": "assistant", "content": "Convert the nonempty text values with DATEVALUE, then count the resulting dates."},
{"role": "user", "content": "Can you show the steps using a helper column?"},
{"role": "assistant", "content": 'In C2 enter =IF(A2="","",DATEVALUE(A2)), fill down to C100, then use =COUNTIFS(C2:C100,"<"&TODAY()+B1,C2:C100,">0").'},
]
template = Path(
hf_hub_download(model_id, "representation_prompt.txt")
).read_text(encoding="utf-8").strip()
history = "\n".join(f"[{m['role']}]: {m['content']}" for m in conversation)
prompt = template.format(conversation_history=history)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
tokenize=True,
add_generation_prompt=True,
enable_thinking=False,
return_tensors="pt",
return_dict=True,
)
prompt_length = inputs["input_ids"].shape[1]
if prompt_length > 8192:
raise ValueError("The formatted prompt exceeds the 8,192-token limit.")
model = AutoModelForImageTextToText.from_pretrained(
model_id,
dtype=torch.float32 if device == "cpu" else torch.bfloat16,
device_map=device,
).eval()
with torch.inference_mode():
output = model.generate(
**inputs.to(device),
max_new_tokens=1024,
do_sample=False,
temperature=None,
top_p=None,
top_k=None,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
text = tokenizer.decode(output[0, prompt_length:], skip_special_tokens=True).strip()
representation = json.loads(text)
if (
not isinstance(representation, dict)
or set(representation) != {"task", "user_behavior"}
or not all(isinstance(value, str) and value.strip() for value in representation.values())
):
raise ValueError("Expected nonempty task and user_behavior strings.")
print(json.dumps(representation, indent=2))
The standalone example script runs the same conversation and also accepts a JSON list of role/content messages:
hf download microsoft/UserRepGen-4B example.py --local-dir userrepgen-example
python userrepgen-example/example.py --device cuda
# To use your own authorized conversation:
python userrepgen-example/example.py --device cuda --conversation conversation.json
Use --device cpu when a suitable GPU is not available. The script accepts --model /path/to/local/model for an already downloaded model directory. While the repository is private, authenticate with hf auth login using an account with read access.
The example uses greedy decoding for a simple smoke test, not the sampling settings used for the paper's evaluations. Invalid JSON or missing/empty fields raise an error rather than silently repairing the model's output.
The following is the actual output from the standalone example above, run on CPU using the published Hugging Face checkpoint. The prompt contained 481 tokens and generation produced 274 tokens, including the end-of-sequence token. Outputs may vary with runtime, hardware, or generation settings.
Show generated task and user behavior
{
"task": "The user seeks an Excel formula to count dates within a specific range (A2:A100) that fall before a calculated threshold (today plus a value from B1), while handling edge cases where the source data is stored as text, contains empty cells, and requires a step-by-step solution involving a helper column.",
"user_behavior": "The user initiates with a direct, concise request for a specific formula but immediately pivots to troubleshooting when the initial solution fails, providing brief, factual feedback about the nature of the data (e.g., 'text', 'empty cells') rather than emotional reactions. They decompose the problem incrementally across turns, asking for specific implementation details like 'steps using a helper column' rather than requesting a complete solution upfront. Their communication style is terse and functional, avoiding pleasantries or verbose explanations. They do not provide extensive context initially but reveal constraints (like data types) only after the assistant proposes a solution that doesn't work. They engage in a multi-turn iterative process, refining the request based on the assistant's output, and do not terminate the conversation prematurely or ask for unrelated tasks. They prefer the assistant to handle the logic and provide the formula, but they will explicitly request step-by-step instructions if the assistant's output is too abstract or if they need to verify the logic manually."
}
This is an inferred description, not verified facts about a person. Even this example includes task-specific details and interpretations that go beyond directly observed behavior. Review representations before sharing or reusing them, as described in the limitations below.
The reported inference setup uses Python, PyTorch, and a Qwen3.5-compatible runtime such as Hugging Face Transformers or vLLM. The model was hosted with vLLM on NVIDIA H100, A100, RTX A6000, and B200 GPUs. Other hardware and operating systems were not evaluated in the research. A Linux CPU smoke test with Transformers 5.2.0, PyTorch 2.11.0, and Accelerate 1.10.1 successfully loaded the checkpoint and generated valid JSON for a local WildChat conversation. This is a functional check, not a CPU performance benchmark. The published standalone example was also tested end-to-end using the Hugging Face repository ID, rather than a local model directory. CPU inference uses float32 and requires substantially more memory than the approximately 9.1 GB weight file. No optimized or device-specific formats are currently available.
The checkpoint retains the Qwen3.5 conditional-generation architecture, so the example uses AutoModelForImageTextToText, even though UserRepGen-4B was fine-tuned and evaluated for text-only inputs. Image and video use are not supported by this release.
Configuration and tokenizer/chat-template assets come from Qwen3.5-4B revision 851bf6e. All 723 stored tensor names and shapes were checked against that configuration; the output head shares the input-embedding weights. The upstream assets retain their Apache-2.0 license. The representation prompt and conversation formatting match the training pipeline. The outer chat message has the user role, and enable_thinking=False disables the backbone's reasoning mode.
The training limits were 8,192 prompt tokens and 1,024 completion tokens. Extended-context use beyond the 9,216-token training sequence length is not supported by this release, and long-context performance was not separately benchmarked.
UserRepGen-4B is intended for research and development involving LLM-based user simulation. Its primary use is to transform an authorized conversation into a compact natural-language representation that can condition a separate user simulator. This can support conversation reconstruction, simulator evaluation, interactive training and evaluation of AI assistants, and research into behavioral diversity in simulated user populations.
The model is most appropriate for English, multi-turn user-assistant conversations similar to the seven evaluation datasets described below. The intended audience includes academic and industry researchers, engineers, and collaborators working on human-AI interaction, user simulation, or replication and extension of the accompanying research.
The learned behavior component may be combined with a different task description to study whether observed interaction patterns transfer to a new scenario. Representations can also support research into population-level behavioral diversity in simulations. Downstream fidelity depends on the separate simulator and its environment, not just the representation.
The model is not intended for non-consensual profiling, surveillance, impersonation, deceptive automated engagement, identity discovery, or linking records about a person without authorization. Representations must not be treated as verified, exhaustive, stable, or clinically meaningful descriptions of an individual. They must not be treated as anonymized data: the research shows that learned behavior descriptions can increase the ability to link conversations from the same user.
The model was not designed or evaluated for consequential decisions in areas such as employment, education, housing, credit, insurance, healthcare, legal services, law enforcement, or access to essential services. It was not evaluated for non-English inputs, short or single-turn interactions, modalities other than text, autonomous deployment, demographic inference, real-world identity recovery, or faithful simulation outside user-assistant conversations.
Synthetic simulations should not replace research with real users or be presented as genuine human feedback or evidence. The model must not be used to operate deceptive automated accounts, manipulate online discussions or engagement signals, contaminate datasets with interactions misrepresented as human-generated, or fabricate research evidence.
Inference from limited evidence: A representation is inferred from one conversation and may overgeneralize absence or a one-time behavior into a stable rule about the user.
Unsupported interpretations: Outputs may contain assumptions not grounded in the source conversation. The analysis found that the trained model can replace broad personality claims with behavioral rules that are still too confident.
Privacy and linkability: Representations may preserve sensitive or identifying details and can facilitate cross-conversation linkage. They are not anonymized data.
Task-specific leakage: Behavior descriptions may include exact commands, tool names, technical artifacts, or termination phrases from the source conversation, which can reduce transfer and expose source content.
Redundancy and length: Outputs are longer on average than base-model outputs and may repeat similar instructions.
Simulator and pipeline dependence: Downstream fidelity depends on the separate user simulator, assistant, prompts, sampling procedure, and judge. Performance may degrade when these differ from the research setup. A good representation does not guarantee a faithful rollout.
Evaluation dependence: The main fidelity metric is produced by an LLM judge. Although an author annotation study found 88% preference agreement, this does not establish agreement across a broader population.
Language and domain coverage: Training retained safe English conversations with at least four user turns. Performance is not established for other languages, shorter interactions, other modalities, or sensitive domains.
Fairness and representation: Better aggregate behavioral diversity does not establish equitable performance across demographic groups. Demographic fairness was not evaluated.
Population coverage: The selected datasets may overrepresent technical users, AI-model evaluators, paid online study participants, public-repository software developers, and users willing to interact through research interfaces. Demographic and age distributions for the selected samples are not reported.
Safety: The fine-tune was not evaluated for standard generative-model safety categories such as hateful, sexual, violent, or self-harm content, jailbreak robustness, or copyright memorization.
UserRepGen-4B was trained to generate user representations that optimize downstream simulation fidelity. There was no explicit alignment objective. The training pipeline retained safe English conversations and used natural-language hindsight feedback from Qwen3.5-4B- and Gemma3-4B-based simulation environments. These measures reduce exposure to unsafe training examples but do not establish safety alignment, privacy protection, factual grounding, or resistance to malicious use. The base model's alignment properties may still apply, but were not independently characterized for this fine-tune in the manuscript.
Training data was preprocessed to retain safe conversations, and model outputs were manually analyzed for safety during development. Harmful outputs were not observed when using known-safe inputs. However, the model was not specifically trained to prevent the generation of harmful content, privacy and sensitive-information disclosure, or reproduction of copyrighted or other protected materials when such content is present in user-provided inputs. Users should evaluate and select input datasets with this behavior in mind.
Developers should process only conversations for which they have authorization and a valid purpose, minimize or remove identifying and sensitive information, and assess whether creating a persistent behavioral representation is necessary. Generated descriptions should be reviewed for unsupported inferences, sensitive-content retention, stereotyping, and task-specific leakage before they are shared or reused. Access to source conversations, representations, and simulated conversations should be restricted and audited.
The model is intended for research, batch-processing, and human-supervised user simulation or evaluation workflows. For any consequential or high-risk application, developers should perform application-specific legal and responsible-AI review, evaluate performance with affected users, provide meaningful human oversight, and avoid treating synthetic behavior as a substitute for real-user evidence. Such review does not establish suitability for the out-of-scope uses described above.
Training uses a fixed set of 3,200 English, multi-turn user-assistant conversations drawn from seven datasets:
Real-world interactions: WildChat, LMSYS-Chat-1M, PRISM, SWE-Chat, and ThoughtTrace.
Simulated interactions: WildChat Coding and WildChat Writing.
Approximately 500 conversations are used from each dataset, except ThoughtTrace, from which 200 are used. Preprocessing removes near-duplicate conversations based on n-gram overlap and retains safe English conversations with at least four user turns. Training also uses AI-generated initial representations, simulated conversations, hindsight feedback, and improved-representation targets.
The source datasets differ materially in collection setting and population. WildChat and LMSYS-Chat-1M contain opt-in interactions from publicly accessible chatbot services; PRISM and ThoughtTrace were collected through paid Prolific studies; SWE-Chat contains public-repository coding-agent sessions; and WildChat Coding and WildChat Writing contain model-simulated interactions grounded in goals extracted from real WildChat conversations. These differences should be considered when assessing representativeness, consent, privacy, and fitness for a downstream use.
The model adapts Qwen3.5-4B using rank-64 LoRA and online supervised fine-tuning (OSFT) through hindsight distillation:
The current policy generates a representation from a source conversation.
Fixed simulation environments use the representation to produce simulated conversations.
Judges compare the simulated and original user behavior.
Natural-language hindsight feedback is used to generate an improved representation.
The improved representation serves as the supervised target for an online update.
Unlike approaches that optimize the simulator's policy, this method optimizes the reusable representation on which a simulator is conditioned. Training used up to 8,192 prompt tokens and 1,024 completion tokens.
The primary evaluation uses a disjoint sample of 700 conversations, with 100 conversations from each of the seven datasets above. GPT-5.4 mini serves as the user simulator, assistant, and fidelity judge, providing an evaluation environment not used to produce training feedback.
Each representation generator receives the same source conversation. A simulator conditioned on its representation interacts with an assistant until termination or a maximum conversation length. A separate judging call compares the original and simulated user behavior across communication style, engagement, requests, responses, and context, assigning an overall fidelity score from 1 to 10. Three rollouts are sampled per representation, and results are reported as best-of-1 and best-of-3.
Human validation uses 100 pairs of simulations with unequal judge scores. An expert annotator, blinded to model identity and presentation order, selects the more faithful simulation; preference agreement with the judge is 88%.
Higher scores indicate better fidelity on the 1-10 judging scale.
Representation generator / training method
Best-of-1
Best-of-3
Qwen3.5-4B base model
3.41
4.42
GPT-5.5
3.90
4.90
Supervised fine-tuning (SFT)
4.64
5.68
OPSD
4.46
5.60
GRPO
3.57
4.59
UserRepGen-4B (OSFT)
4.95
6.02
UserRepGen-4B improves on GPT-5.5, the strongest evaluated off-the-shelf model, by 1.05 best-of-1 and 1.12 best-of-3 points.
Separately, applying iterative hindsight feedback to the base model at test time raises fidelity from 3.41/4.42 initially to 6.10 best-of-1 after ten rounds and a peak of 7.07 best-of-3 after nine rounds. These results use additional test-time simulation and feedback, unlike direct UserRepGen-4B inference.
Cross-task generalization: Experiments use 147 ThoughtTrace users with at least two conversations who do not appear in training. Task descriptions come from target conversations, while behavior descriptions come from no conversation, a different user, another conversation from the same user, or the target conversation. Learned behavior descriptions improve fidelity for unseen tasks from the same user.
Population diversity: Learned behavior descriptions reduce population-level Jensen-Shannon divergence and Self-BLEU relative to task-only conditioning, indicating closer behavioral-distribution matching and less homogeneous simulated conversations. Behavioral-distribution evaluation uses Qwen3.5-122B-A10B-FP8 descriptions across six dimensions, Qwen3-Embedding-8B embeddings, and k-means with 500 clusters.
Same-user re-identification: Over 147 ThoughtTrace users, GPT-5.4 mini selects which of four candidates comes from the same user as a query. UserRepGen-4B behavior descriptions yield 51.02% accuracy, compared with 44.22% using full conversations and 25% for random selection. This result demonstrates a linkability risk, not anonymization or suitability for identity discovery.
Training compute totals and a carbon-emissions estimate are not provided in this model card. The inference hardware listed above should not be interpreted as a training-compute or emissions estimate.
This research was conducted by members of Microsoft Research. We welcome feedback and collaboration. To share suggestions, ask questions, or report unexpected or offensive behavior, contact:
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("microsoft/UserRepGen-4B", revision="bfc905b249365cab00e94ba308f2cea5b23bbc6e")
model = AutoModelForCausalLM.from_pretrained("microsoft/UserRepGen-4B", revision="bfc905b249365cab00e94ba308f2cea5b23bbc6e", device_map="auto")
microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.textSubject: 
