trillion-labs
TheBioCollection
TheBioCollection
TheBioCollection is a 52.6B-token pretraining-scale corpus for biology that transforms heterogeneous biological resources into LLM training-friendly data. It is built through a construction pipeline that collects resources across biological domains, refines them through deduplication, entity tagging and augmentation, enriches them with tool-computed biological properties, and render them as instruction-form data with programmatically verifiable answers. The… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/TheBioCollection.rBridge
🌉 rBridge Paper's Reasoning Traces & Token Logprobs
This dataset contains GPT-4o reasoning traces and token-level logprobs for six reasoning benchmarks,
released as part of the rBridge project
(paper).
rBridge uses these traces as gold-label reasoning references. By computing a weighted negative log-likelihood
over these traces — where each token is weighted by the frontier model's confidence — small proxy models (≤1B)
can reliably predict the reasoning performance of much larger… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/rBridge.sim-scholar-eval-artifacts
eval/ — output directory index
Auto-generated map. Metrics: <dir>/<surface>/<bench>/pf_<model>.json; trajectories: <dir>/traj/.
dir
run/ckpt
benches
surfaces
#models
#valid
notes
bandps_v2_local
?
internal_citation_holdout,internal_known_item,litsearch
untagged
1
9
SEPARATE: band+paper_set v2 internal
bandps_v2_sweep
?
litqa2_validation,paper_finder_litqa2_validation,paper_finder_validation
untagged
1
9
SEPARATE: band+paper_set v2 sweep
concat_v1_local
?… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/sim-scholar-eval-artifacts.TheBioCollection-Eval
TheBioCollection-Eval
TheBioCollection-Eval is a biological evaluation suite for assessing large language models (BioLMs) for biology across small molecules, proteins, genomic sequences, cells/pathways, and cross-domain reasoning. It is constructed by drawing subtasks from many scattered existing benchmarks (Mol-Instructions, MolLangBench, BioReason-Pro, PerturBench Replogle K562) and combining them with source-derived newly-constructed instruction datasets.
Evaluation… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/TheBioCollection-Eval.sim-scholar-qa-archiveSimScholar-SFT
S3 SFT Trajectories
Complete ReAct trajectories for scientific-literature search.
Code ·
S3 collection ·
Source corpus
This dataset contains 14,633 single- and two-hop tool-use trajectories. In each
trajectory, a policy searches and reads a fixed scientific corpus through nine
tools, then submits an answer with a correctness label. The messages column
uses OpenAI tool-calling chat format.
At a glance
Question type
Rows
Correct
Incorrect
Single-hop… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/SimScholar-SFT.