News! We released LLM Reasoners, a library for complex reasoning with LLMs, and include the code to reproduce some experiments in RAP. Give it a try!
Source code for the paper Reasoning with Language Model is Planning with World Model
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Warning: This code only supports LLaMA-1. Check our new library LLM Reasoners for more flexible choices of LLMs.
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Our experiments are conducted with LLaMA-33B, which takes at least 4 GPUs of 24GB memory each. The code also supports smaller LLaMA models, but other LLMs (e.g. those from Hugging Face) are not tested.
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Acquire the checkpoints of LLaMA from MetaAI following the LLaMA official repo and set up the environment variable:
export LLAMA_CKPTS="YOUR_PATH_TO_LLAMA_CHECKPOINTS" -
Install all required packages for LLaMA official repo.
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(For Blocksworld) Install all required packages for GPT-Plan-Benchmark.
- Set up
VALfollowing this guide and make sure you set the environment variableexport VAL="YOUR_PATH_TO_VAL" - Run the command:
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --master_port 1034 --nproc_per_node 4 run_blocksworld.py --task mcts --model_name LLaMA --ckpt_path $LLAMA_CKPTS/30B --verbose True --data data/blocksworld/step_4.json --max_depth 4 --name run_4_May26_max_depth_4_alpha_05_rollouts_10 --rollouts 10
- Run with:
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1054 run_gsm8k.py --llama-ckpt $LLAMA_CKPTS/30B --speedup-confidence-batch-size 2 - Use
python run_gsm8k.py -- --helpfor details about arguments - For RAP-Aggregation, after running RAP on GSM8k, run
python aggregate_gsm8k.py --log-dir <log_dir>
- Run with:
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1074 run_prontoqa.py --llama-ckpt $LLAMA_CKPTS/30B - Use
python run_prontoqa.py -- --helpfor details about arguments