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This document provides extensive examples demonstrating how to use oat 🌾 to (1) run various direct optimizers, (2) integrate different preference oracles, and (3) implement diverse active exploration algorithms. All the examples are tested on a machine with 8 A100 GPUs, with training logs publicly available on wandb for reproducibility.

First of all, you could always check all supported arguments by running:

python -m oat.experiment.main -h

Direct optimizers

oat currently supports DPO, IPO, SLiC, and SimPO by setting --dap-algo. Remember to adjust the associated hyper-parameter beta.

python -m oat.experiment.main \
+   --dap-algo IPO \
+   --beta 0.1 \
    # other flags...

Preference oracles

Locally hosted Mosec service

In the main page we have shown the usage of pairrm as the preference oracle, which runs in the same process as the actor. Next, we give an example of training online DPO with a preference oracle served using Mosec.

First, we start the Mosec service locally, which will serve 4 Skywork/Skywork-Reward-Llama-3.1-8B parallel workers as the preference oracle on the first 4 GPUs:

MOSEC_LOG_LEVEL=debug python -m oat.oracles.remote.server --cuda-devices 0,1,2,3

After the service is up (seeing "http service is running" from the log), start a new bash and run:

python -m oat.experiment.main \
    --flash-attn \
    --gradient-checkpointing \
    --rnd-seed \
    --gpus 8 \
    --dap-algo DPO \
    --beta 0.1 \
+   --preference-oracle remote \
+   --remote-rm-url http://0.0.0.0:8000 \
    --pretrain trl-lib/pythia-1b-deduped-tldr-sft \
    --prompt-data lkevinzc/tldr-with-sft-reference \
    --input-key prompt \
    --output-key pythia-1b-reference \
    --sync-params-every 1 \
    --max-train 50000 \
    --generate-max-length 53 \
    --train-batch-size 128 \
    --rollout-batch-size 128 \
    --rollout-batch-size-per-device 32 \
    --pi-buffer-maxlen-per-device 32 \
    --train-batch-size-per-device 8 \
    --eval-steps 20 \
    --use-wb \
    --wb-run-name 1b_skywork_dpo_online

OpenAI API for GPT-as-a-judge

Alternatively, we could also query OpenAI API to use GPT-as-a-judge as the preference oracle:

python -m oat.experiment.main \
    --flash-attn \
    --gradient-checkpointing \
    --rnd-seed \
    --gpus 8 \
+   --collocate \
    --dap-algo DPO \
    --beta 0.1 \
+   --preference-oracle gpt-4o-mini-2024-07-18 \
    --pretrain trl-lib/pythia-1b-deduped-tldr-sft \
    --prompt-data lkevinzc/tldr-with-sft-reference \
    --input-key prompt \
    --output-key pythia-1b-reference \
    --sync-params-every 1 \
    --max-train 50000 \
    --generate-max-length 53 \
    --train-batch-size 128 \
    --rollout-batch-size 128 \
    --rollout-batch-size-per-device 32 \
    --pi-buffer-maxlen-per-device 32 \
    --train-batch-size-per-device 8 \
    --eval-steps 20 \
    --use-wb \
    --wb-run-name 1b_gpt_4o_mini_dpo_online

We enabled collocation of learner and actor workers given the abundant GPU memory, thanks to the fact that the preference oracle (GPT) runs on OpenAI's side and almost takes no resource on our machines.

Scale up with remote Mosec service

Likewise, we can also host our own remote server for any reward model on a separate machine, utilizing more compute to train larger models. With a Kubernetes-managed cluster, you could follow these steps to serve a remote preference oracle at http://remote-rm. Otherwise, you may need to obtain the remote machine's IP address (e.g., 10.0.0.1), and set --remote-rm-url http://10.0.0.1:8000 accordingly.

python -m oat.experiment.main \
    --flash-attn \
    --gradient-checkpointing \
    --rnd-seed \
    --gpus 8 \
    --dap-algo DPO \
    --beta 0.1 \
    --preference-oracle remote \
+   --remote-rm-url http://remote-rm \
+   --pretrain trl-lib/pythia-6.9b-deduped-tldr-sft \
    --prompt-data lkevinzc/tldr-with-sft-reference \
    --input-key prompt \
+   --output-key pythia-6.9b-reference \
    --sync-params-every 1 \
    --max-train 50000 \
    --generate-max-length 53 \
    --train-batch-size 128 \
    --rollout-batch-size 128 \
    --rollout-batch-size-per-device 32 \
    --pi-buffer-maxlen-per-device 32 \
    --train-batch-size-per-device 8 \
    --eval-steps 20 \
    --use-wb \
+   --wb-run-name 6.9b_skywork_dpo_online

LLM exploration algorithms

All examples below assume a locally served preference oracle as done in the section above.

[SEA] Sample-Efficient Alignment for LLMs

Note

Paper: https://arxiv.org/pdf/2411.01493.

You can find a thorough comparison between all algorithms mentioned in this section in our paper.

Oat natively supports SEA using the oat.experiment.main entry script:

python -m oat.experiment.main \
    --flash-attn \
    --gradient-checkpointing \
    --rnd-seed \
    --gpus 8 \
    --dap-algo DPO \
    --beta 0.1 \
    --preference-oracle remote \
    --remote-rm-url http://0.0.0.0:8000 \
    --pretrain trl-lib/pythia-1b-deduped-tldr-sft \
    --prompt-data lkevinzc/tldr-with-sft-reference \
    --input-key prompt \
    --output-key pythia-1b-reference \
    --sync-params-every 1 \
    --max-train 50000 \
    --generate-max-length 53 \
    --train-batch-size 128 \
    --rollout-batch-size 128 \
    --rollout-batch-size-per-device 32 \
    --pi-buffer-maxlen-per-device 32 \
    --train-batch-size-per-device 8 \
    --eval-steps 20 \
+   --num-prompt-epoch 2 \
+   --max-step-adjustment 0.75 \
+   --lr-warmup-ratio 0.02 \
+   --eval-query-interval 2560 \
+   --num-samples 20 \
+   --learn-rm \
+   --exp-method EnnBAITS \
+   --model-rollout \
+   --max-model-data-ratio 0.3 \
    --use-wb \
    --wb-run-name 1b_skywork_dpo_sea

[EE4LLM] Efficient Exploration for LLMs

Note

Paper: https://arxiv.org/pdf/2402.00396

Run EE4LLM by disabling policy learning and enabling best-of-n sampling for evaluation:

python -m oat.experiment.main \
    --flash-attn \
    --gradient-checkpointing \
    --rnd-seed \
    --gpus 8 \
    --dap-algo DPO \
    --beta 0.1 \
    --preference-oracle remote \
    --remote-rm-url http://0.0.0.0:8000 \
    --pretrain trl-lib/pythia-1b-deduped-tldr-sft \
    --prompt-data lkevinzc/tldr-with-sft-reference \
    --input-key prompt \
    --output-key pythia-1b-reference \
    --sync-params-every 1 \
    --max-train 50000 \
    --generate-max-length 53 \
    --train-batch-size 128 \
    --rollout-batch-size 128 \
    --rollout-batch-size-per-device 32 \
    --pi-buffer-maxlen-per-device 32 \
    --train-batch-size-per-device 8 \
    --eval-steps 20 \
+   --num-samples 20 \
+   --learn-rm \
+   --learn_rm_only \
+   --exp-method EnnEETS \
+   --exp_rnd_sample \
+   --online_evaluation \
+   --best_of_n_eval \
+   --num_bon 10 \
    --use-wb \
+   --wb-run-name 1b_skywork_dpo_ee4llm

[APL] Active Preference Learning for Large Language Models

Note

Paper: https://arxiv.org/pdf/2402.08114

APL can be implemented by inheriting oat's learner and actor classes (codes). Run it with a dedicated entry script:

+ python -m oat.experiment.run_apl \
    --flash-attn \
    --gradient-checkpointing \
    --rnd-seed \
    --gpus 8 \
    --dap-algo DPO \
    --beta 0.1 \
    --preference-oracle remote \
    --remote-rm-url http://0.0.0.0:8000 \
    --pretrain trl-lib/pythia-1b-deduped-tldr-sft \
    --prompt-data lkevinzc/tldr-with-sft-reference \
    --input-key prompt \
    --output-key pythia-1b-reference \
    --sync-params-every 1 \
    --max-train 50000 \
    --generate-max-length 53 \
    --train-batch-size 128 \
    --rollout-batch-size 128 \
    --rollout-batch-size-per-device 32 \
    --pi-buffer-maxlen-per-device 32 \
    --train-batch-size-per-device 8 \
    --eval-steps 20 \
+   --num_prompt_epoch 4 \
+   --max_train 100000 \
+   --max_step_adjustment 0.125 \
+   --num_samples 8 \
+   --apl_pref_certainty_only \
    --use-wb \
+   --wb-run-name 1b_skywork_apl

[XPO] Exploratory Preference Optimization

Note

Paper: https://arxiv.org/pdf/2405.21046

XPO can be implemented by inheriting oat's learner and actor classes (codes). Run it with a dedicated entry script:

+ python -m oat.experiment.run_xpo \
    --flash-attn \
    --gradient-checkpointing \
    --rnd-seed \
    --gpus 8 \
    --dap-algo DPO \
    --beta 0.1 \
    --preference-oracle remote \
    --remote-rm-url http://0.0.0.0:8000 \
    --pretrain trl-lib/pythia-1b-deduped-tldr-sft \
    --prompt-data lkevinzc/tldr-with-sft-reference \
    --input-key prompt \
    --output-key pythia-1b-reference \
    --sync-params-every 1 \
    --max-train 50000 \
    --generate-max-length 53 \
    --train-batch-size 128 \
    --rollout-batch-size 128 \
    --rollout-batch-size-per-device 32 \
    --pi-buffer-maxlen-per-device 32 \
    --train-batch-size-per-device 8 \
    --eval-steps 20 \
    --use-wb \
+   --wb-run-name 1b_skywork_xpo