Aviary: training language agents on challenging scientific tasks
Paper · 2024 · Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths et al.
Summary
Scientific work usually unfolds over many rounds of tool use, analysis and feedback, yet software for building and training LLM-based agents for such multi-step work is fragmented and loosely defined. The authors formalize agent tasks as language decision processes (LDPs). These are POMDPs whose actions and observations are expressed as text. Agents themselves are represented as stochastic computation graphs, so components such as weights, prompts or sampling temperature can be optimized. On this basis they release two open-source libraries. Aviary is a gym-style collection of five environments: GSM8K, HotpotQA, molecular-cloning questions (SeqQA), literature-based question answering (LitQA2) and protein stability engineering. LDP provides agents, optimizers and training code. They fine-tune Llama-3.1-8B-Instruct using behavior cloning and expert iteration, then add majority voting at inference time. The resulting small open model rivals or beats Claude 3.5 Sonnet agents and PhD-level humans on SeqQA and LitQA2, often at a fraction of the cost. This suggests modest models and budgets can support high-throughput automation of real biology tasks.
Key points
- Five environments: Aviary includes GSM8K and HotpotQA recast as environments, plus three biology environments.
- DNA construct and cloning questions (SeqQA)
- Answering multiple-choice research questions from the literature (LitQA2)
- Proposing mutations that make proteins more stable
- Training method: Llama-3.1-8B EI agents are trained with expert iteration and use majority voting over many rollouts, up to about 1000 per task.
- On SeqQA, they match the accuracy of majority-voted Claude 3.5 Sonnet at roughly 100x lower cost.
- On LitQA2, they surpass single-rollout Sonnet at about 3x lower cost.
- Cost comparison: A SeqQA trajectory needs 7–10 LLM calls.
- Claude 3.5 Sonnet costs about $0.07 per trajectory.
- Llama-3.1-8B EI costs about $0.00066 per trajectory.
- Human PhD contractors cost $4–12 per question.
- Limitations: The authors use evaluation splits that are unlikely to appear in pre-training data, because LitQA2 has been re-hosted online without its canary string. They also note that comparisons with humans are imperfect, since humans had access to different tools.
- Code: Both libraries are available at github.com/Future-House/aviary and github.com/Future-House/ldp.
Institutions
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 works
Cite
Cite as Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, Manu Ponnapati, Albert Bou, Jon Laurent, Ori Kabeli, Geemi Wellawatte, Sam Cox, Samuel G. Rodriques and Andrew D. White. 2024. Aviary: training language agents on challenging scientific tasks. arXiv:2412.21154.
@misc{narayanan2024aviary,
title = {{Aviary: training language agents on challenging scientific tasks}},
author = {Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, Manu Ponnapati, Albert Bou, Jon Laurent, Ori Kabeli, Geemi Wellawatte, Sam Cox, Samuel G. Rodriques and Andrew D. White},
year = {2024},
doi = {10.48550/arxiv.2412.21154},
eprint = {2412.21154},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2412.21154}
}References
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