SSD

Simple Self-Distillation (SSD) is described in Embarrassingly Simple Self-Distillation Improves Code Generation.

SSD samples completions from the model at a training-time temperature and truncation configuration, then fine-tunes on those raw, unverified samples with standard cross-entropy loss. It requires no reward model, verifier, teacher model, or reinforcement learning — only a set of problem prompts and the model itself.

In the current TRL implementation:

• the model generates completions at a specified training-time temperature (temperature) and truncation (top_k, top_p)
• the dataset only requires a prompt column
• training uses standard cross-entropy loss on the generated completions
• empty or single-line stub completions are filtered by default (filter_empty=True)
• the evaluation-time temperature and truncation are set independently at inference time
• vLLM can be used for faster generation via use_vllm=True (see vLLM integration)

Usage

from datasets import Dataset

from trl.experimental.ssd import SSDConfig, SSDTrainer

dataset = Dataset.from_dict(
    {
        "prompt": [
            [{"role": "user", "content": "Write a function to add two numbers."}],
            [{"role": "user", "content": "Write a function to check if a number is prime."}],
        ],
    }
)

training_args = SSDConfig(
    output_dir="ssd-model",
    temperature=0.6,           # T_train from the paper
    top_k=20,                  # training-time top-k truncation
    top_p=0.95,                # training-time top-p truncation
    max_completion_length=65536,
    learning_rate=5e-6,
)

trainer = SSDTrainer(
    model="Qwen/Qwen3-4B-Instruct",
    args=training_args,
    train_dataset=dataset,
)
trainer.train()

Expected dataset columns

Each example must provide:

• prompt: the problem prompt (string or conversational format)

No privileged_context, reward functions, or teacher model are needed.

Key hyperparameters

The paper identifies the following key hyperparameters:

• temperature: training-time sampling temperature (T_train). Higher values create more diverse samples but may include more noise. The paper uses T_train=0.6 with truncation.
• top_k and top_p: training-time truncation parameters (rho_train). These suppress low-probability distractor tails during data synthesis.
• T_eval: the evaluation-time decoding temperature is set independently at inference time. The paper shows that T_train and T_eval compose through an effective temperature T_eff = T_train * T_eval, with a broad optimal band.

Example script

Use trl/experimental/ssd/ssd.py to launch SSD training from the command line. The script supports any causal LM from the Hub, custom local datasets via --dataset_path, and PEFT/LoRA via the standard ModelConfig flags.

python trl/experimental/ssd/ssd.py \
    --model_name_or_path Qwen/Qwen3-4B-Instruct-2507 \
    --dataset_name microsoft/rStar-Coder \
    --dataset_config seed_sft \
    --prompt_column question \
    --output_dir outputs/ssd-qwen3-4b \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 32 \
    --learning_rate 5e-6 \
    --lr_scheduler_type cosine \
    --max_prompt_length 1024 \
    --max_completion_length 65536 \
    --temperature 1.6 \
    --top_k 20 \
    --top_p 0.8 \
    --num_train_epochs 1 \
    --bf16 \
    --report_to trackio

Evaluation on LiveCodeBench

Use trl/experimental/ssd/ssd_eval.py to evaluate a base model or an SSD-trained checkpoint on LiveCodeBench v6. The script uses vLLM for generation and LiveCodeBench's official codegen_metrics for sandboxed pass@k scoring; default decoding parameters match Table 3 of the paper.

python trl/experimental/ssd/ssd_eval.py \
    --model_name_or_path <path-or-repo> \
    --temperature 1.1 --top_k 20 --top_p 0.8 \
    --n 1 \
    --output_file outputs/lcb_v6.json

SSDConfig

[[autodoc]] experimental.ssd.SSDConfig

SSDTrainer

[[autodoc]] experimental.ssd.SSDTrainer - train - save_model - push_to_hub