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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# /// script
# dependencies = [
# "trl[peft]",
# "trackio",
# "kernels",
# ]
# ///
"""
Triple Preference Optimization (TPO) training.
TPO requires a *triple-preference* dataset where each example contains a `chosen`, a `rejected` and a `reference`
(gold) completion for the same prompt. Two dataset paths are supported out of the box:
- Use the published
[`tpo-alignment/triple-preference-ultrafeedback-40K`](https://huggingface.co/datasets/tpo-alignment/triple-preference-ultrafeedback-40K)
dataset directly. It already has the `prompt` / `reference` / `chosen` / `rejected` schema.
- Pass `--dataset_name openbmb/UltraFeedback` and the script automatically builds the triple-preference dataset as
described in the TPO paper (Saeidi et al., 2025): the response with the highest `overall_score` becomes `reference`,
the second-highest becomes `chosen`, and the lowest becomes `rejected`.
In both cases, if the dataset is in standard (plain-string) format it is auto-wrapped into the conversational format so
that the model's chat template is applied — this matches how Instruct models like `Qwen/Qwen3-0.6B` are trained.
Usage:
Full training:
```bash
python examples/scripts/tpo.py \
--dataset_name tpo-alignment/triple-preference-ultrafeedback-40K \
--model_name_or_path Qwen/Qwen3-0.6B \
--per_device_train_batch_size 2 \
--max_steps 1000 \
--learning_rate 5e-7 \
--gradient_accumulation_steps 8 \
--beta 0.01 \
--tpo_alpha 1.0 \
--output_dir Qwen3-0.6B-TPO \
--no_remove_unused_columns
```
TPO-L (length-normalized variant with target reward margin):
```bash
python examples/scripts/tpo.py \
--dataset_name tpo-alignment/triple-preference-ultrafeedback-40K \
--model_name_or_path Qwen/Qwen3-0.6B \
--per_device_train_batch_size 2 \
--max_steps 1000 \
--learning_rate 5e-7 \
--gradient_accumulation_steps 8 \
--beta 0.01 \
--tpo_alpha 1.0 \
--loss_type tpo-l \
--tpo_l_gamma 0.5 \
--output_dir Qwen3-0.6B-TPO-L \
--no_remove_unused_columns
```
LoRA:
```bash
python examples/scripts/tpo.py \
--dataset_name tpo-alignment/triple-preference-ultrafeedback-40K \
--model_name_or_path Qwen/Qwen3-0.6B \
--per_device_train_batch_size 2 \
--max_steps 1000 \
--learning_rate 5e-6 \
--gradient_accumulation_steps 8 \
--output_dir Qwen3-0.6B-TPO-LoRA \
--no_remove_unused_columns \
--use_peft \
--lora_r 32 \
--lora_alpha 16
```
"""
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
from trl import ModelConfig, ScriptArguments, get_kbit_device_map, get_peft_config, get_quantization_config
from trl.experimental.tpo import TPOConfig, TPOTrainer
def build_triple_preference_from_ultrafeedback(example):
"""
Build a TPO triple-preference example from a raw UltraFeedback row.
Following the TPO paper (Saeidi et al., 2025), completions are sorted by `overall_score` and we pick:
- the highest-scored response as the gold `reference`,
- the second-highest as `chosen`,
- the lowest as `rejected`.
Emits the *conversational* format so that [`TPOTrainer`] applies the model's chat template automatically (see
`trl.data_utils.is_conversational`). Completions with a missing `overall_score` or `response` are filtered out; if
fewer than 3 valid completions remain, the returned example contains `None` values and should be filtered out
downstream.
"""
scored = [c for c in example["completions"] if c.get("overall_score") is not None and c.get("response")]
if len(scored) < 3:
return {"prompt": None, "reference": None, "chosen": None, "rejected": None}
scored.sort(key=lambda c: c["overall_score"], reverse=True)
return {
"prompt": [{"role": "user", "content": example["instruction"]}],
"reference": [{"role": "assistant", "content": scored[0]["response"]}],
"chosen": [{"role": "assistant", "content": scored[1]["response"]}],
"rejected": [{"role": "assistant", "content": scored[-1]["response"]}],
}
def to_conversational(example):
"""
Wrap a standard-format triple-preference example (plain strings) in the *conversational* format, so that
[`TPOTrainer`] applies the model's chat template automatically. This is the format expected by Instruct models; for
non-Instruct base models the standard format can be used directly.
"""
return {
"prompt": [{"role": "user", "content": example["prompt"]}],
"reference": [{"role": "assistant", "content": example["reference"]}],
"chosen": [{"role": "assistant", "content": example["chosen"]}],
"rejected": [{"role": "assistant", "content": example["rejected"]}],
}
if __name__ == "__main__":
parser = HfArgumentParser((ScriptArguments, TPOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_into_dataclasses()
################
# Model & Tokenizer
################
dtype = model_args.dtype if model_args.dtype in ["auto", None] else getattr(torch, model_args.dtype)
model_kwargs = dict(
revision=model_args.model_revision,
attn_implementation=model_args.attn_implementation,
dtype=dtype,
)
quantization_config = get_quantization_config(model_args)
if quantization_config is not None:
model_kwargs["device_map"] = get_kbit_device_map()
model_kwargs["quantization_config"] = quantization_config
model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, **model_kwargs)
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
################
# Dataset
################
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
# Auto-build the triple-preference schema from raw UltraFeedback.
first_example = next(iter(dataset[script_args.dataset_train_split]))
if "completions" in first_example and "instruction" in first_example:
dataset = dataset.map(
build_triple_preference_from_ultrafeedback,
remove_columns=list(first_example.keys()),
)
dataset = dataset.filter(lambda ex: ex["reference"] is not None)
first_example = next(iter(dataset[script_args.dataset_train_split]))
# Auto-wrap standard-format triple-preference data (plain strings) into conversational messages so the
# model's chat template gets applied. This matches how Instruct models are trained and is what the TPO
# paper's data preparation produces.
if {"prompt", "chosen", "rejected", "reference"}.issubset(first_example) and isinstance(
first_example["prompt"], str
):
dataset = dataset.map(to_conversational)
################
# Training
################
trainer = TPOTrainer(
model,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
processing_class=tokenizer,
peft_config=get_peft_config(model_args),
)
# train and save the model
trainer.train()
# Run a final evaluation pass and persist the metrics
if training_args.eval_strategy != "no":
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
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