simulation

Agent simulation

Uses a GPT-4 based planner to generate agent trajectories in a simulation environment. The environment consists of a predefined set of simluated tools backed by GPT-4. For example, the search_internet tool pretends to be an internet search engine that generates answers by making a best guess. See package tools for all tools available in the simulation environment.

The GPT-4 based planner, used for driving dataset generation, is a ReAct-style planner that interacts with these tools. The generated trajectories of tool interactions are then converted to a fine-tuning dataset for training smaller, open-source LLMs to mimic the behavior of the GPT-4 based planner. See planner fine-tuning for further details on fine-tuning a planner module with the generated dataset.

Fine-tuned planners can also be evaluated in the simulation environment. By replacing the GPT-4 based planner with a fine-tuned planner, the agent simulation can be used to evaluate the performance of the fine-tuned planner, incl. the effect of prompt masking. It can also be used for evaluating an open-source zero-shot planner that has not been fine-tuned on simulation data.

Setup

Add your OpenAI API key to .env, then export PYTHONPATH=.

Dataset generation

This section describes generation of a planner fine-tuning dataset in a simulation environment. You can also download the complete output of the following commands from here.

Generate requests

Start generating requests (random questions and instructions). First 20 requests are intended to be answered directly by the agent:

python simulation/data/request/single_step.py \
  --output_dir=output/requests \
  --num_requests=20

The next 460 batches of 6 requests each are intended to be answered by the agent in multiple steps. Each batch is on a random topic:

python simulation/data/request/multi_step.py \
  --output_dir=output/requests \
  --num_batches=460 \
  --num_workers=20

Generate trajectories

Run the agent simulation on each generated request and save the trajectories:

python simulation/data/trajectory.py \
  --requests_dir=output/requests \
  --output_dir=output/trajectories \
  --direct_answer=simple \
  --num_workers=20

The --direct_answer=simple option forces the agent answer simple requests directly. These are the 20 single-step requests generated previously. All other requests are answered in multiple steps.

Evaluate trajectories

Evaluate the quality of generated trajectories with GPT-4. Generates ratings from 1 (poor) to 5 (excellent):

python simulation/data/evaluation.py \
  --requests_dir=output/requests \
  --trajectories_dir=output/trajectories \
  --output_dir=output/evaluations \
  --num_workers=20

Package dataset

Package a fine-tuning dataset for the planner module from generated trajectories that have a rating of 4 or higher:

python simulation/data/package.py \
  --requests_dir=output/requests \
  --trajectories_dir=output/trajectories \
  --evaluations_dir=output/evaluations \
  --output_dir=output/dataset \
  --validation_size=30 \
  --rating_threshold=4

Planner evaluation

Instead of running planner evaluation yourself, you can also download the complete output of the following commands from here. Evaluation is done on a separate, smaller dataset consisting of 20 simpler and 30 more complex requests. They can be created with:

python simulation/data/request/single_step.py \
  --output_dir=output-eval/requests \
  --num_requests=20

python simulation/data/request/multi_step.py \
  --output_dir=output-eval/requests \
  --num_batches=5 \
  --num_workers=5

Evaluated are the following planners:

• the GPT-4 based planner used in the previous section but without constraining it to generate direct answers for single-step requests
• a Mistral-7B based fine-tuned planner
• a Mistral-7B-Instruct based zero-shot planner

Since the simulated search_internet and search_wikipedia tools are configured to return no answer with a probability of 0.1 or only a partial answer with a probability of 0.1, four evaluation runs are repeated for each planner.

GPT-4 based planner

for i in {1..4}
do
  python simulation/data/trajectory.py \
    --planner=openai \
    --requests_dir=output-eval/requests \
    --output_dir=output-eval/gpt-4/trajectories_$i \
    --num_workers=20
  python simulation/data/evaluation.py \
    --requests_dir=output-eval/requests \
    --trajectories_dir=output-eval/gpt-4/trajectories_$i \
    --output_dir=output-eval/gpt-4/evaluations_$i \
    --num_workers=20
done

Fine-tuned planner

Evaluated are 8-bit and 4-bit quantized versions of gba-planner-7B-v0.1 (see setup for serving details). The following script shows evaluation of the 8-bit quantized model.

for i in {1..4}
do
  python simulation/data/trajectory.py \
    --planner=finetuned \
    --planner_host=localhost \
    --planner_port=8082 \
    --requests_dir=output-eval/requests \
    --output_dir=output-eval/finetnued-8bit/trajectories_$i \
    --num_workers=5
  python simulation/data/evaluation.py \
    --requests_dir=output-eval/requests \
    --trajectories_dir=output-eval/finetnued-8bit/trajectories_$i \
    --output_dir=output-eval/finetnued-8bit/evaluations_$i \
    --num_workers=5
done

Zero-shot planner

Evaluated is an 8-bit quantized version of the zero-shot planner.

for i in {1..4}
do
  python simulation/data/trajectory.py \
    --planner=zeroshot \
    --planner_host=localhost \
    --planner_port=8081 \
    --requests_dir=output-eval/requests \
    --output_dir=output-eval/zeroshot-8bit/trajectories_$i \
    --num_workers=5
  python simulation/data/evaluation.py \
    --requests_dir=output-eval/requests \
    --trajectories_dir=output-eval/zeroshot-8bit/trajectories_$i \
    --output_dir=output-eval/zeroshot-8bit/evaluations_$i \
    --num_workers=5
done

The statistics over these runs are calculated and summarized in planner_evaluation.ipynb.

Prompt masking

Instead of running this evaluation yourself, you can also download the complete output of the following commands from here. The evaluation re-uses requests generated in the previous section. Evaluated are two planner models (see setup for serving details):

• gba-planner-7B-v0.2, fine-tuned with a loss over the full sequence i.e. prompt and completion tokens
• gba-planner-7B-completion-only-v0.2, fine-tuned with a loss over completion tokens only (prompt masked)

Evaluation of gba-planner-v0.2:

for i in {1..12}
do
  python simulation/data/trajectory.py \
    --planner=finetuned \
    --planner_host=localhost \
    --planner_port=9083 \
    --requests_dir=output-eval/requests \
    --output_dir=output-eval-masking/prompt-and-completion/trajectories_$i \
    --num_workers=5
  python simulation/data/evaluation.py \
    --requests_dir=output-eval/requests \
    --trajectories_dir=output-eval-masking/prompt-and-completion/trajectories_$i \
    --output_dir=output-eval-masking/prompt-and-completion/evaluations_$i \
    --num_workers=5
done

Evaluation of gba-planner-completion-only-v0.2:

for i in {1..12}
do
  python simulation/data/trajectory.py \
    --planner=finetuned \
    --planner_host=localhost \
    --planner_port=9084 \
    --requests_dir=output-eval/requests \
    --output_dir=output-eval-masking/completion-only/trajectories_$i \
    --num_workers=5
  python simulation/data/evaluation.py \
    --requests_dir=output-eval/requests \
    --trajectories_dir=output-eval-masking/completion-only/trajectories_$i \
    --output_dir=output-eval-masking/completion-only/evaluations_$i \
    --num_workers=5
done

Evaluation of the GPT-4 based planner for reference:

for i in {1..12}
do
  python simulation/data/trajectory.py \
    --planner=openai \
    --requests_dir=output-eval/requests \
    --output_dir=output-eval-masking/gpt-4/trajectories_$i \
    --num_workers=20
  python simulation/data/evaluation.py \
    --requests_dir=output-eval/requests \
    --trajectories_dir=output-eval-masking/gpt-4/trajectories_$i \
    --output_dir=output-eval-masking/gpt-4/openai/evaluations_$i \
    --num_workers=20
done

The statistics over these runs are calculated and summarized in planner_evaluation.ipynb.