Reasoning Models

OuterProduct optimizes reasoning model performance over the configurations specified by:

An hpo_spacethat optionally specifies model families and hyper-parameters per family.
An optimizerthat optionally specifies the search strategy over the defined hpo_space.

When you call fit(), all candidate model families in the space are searched with maximum parallelization. If there is one metric specified, the result is a single trained model representing the best configuration found. If multiple metrics are specified, the result is the set of models along the Pareto frontier.

Optimized Reasoning Models At Enterprise Scale

Build optimized reasoning models by tuning over model families, hyper-parameter configurations, and even multiple metrics. The example below shows how to build a reasoning model for regression across the TabM and TabICL model families.

import outerproduct as op

op.init()
ws = op.Workspace()

train_df = ws.table("customers")

# TabM: MLP-style tabular net (https://arxiv.org/abs/2410.24210)
tabm = (
    op.ModelParamSpace(family="tabm")
    .float("lr", 1e-4, 3e-3, log=True)
    .categorical("n_blocks", [1, 2, 3, 4])
    .int("d_block", 64, 512, log=True)
)

# TabICL: tabular foundation model (https://arxiv.org/abs/2602.11139)
tabicl = (
    op.ModelParamSpace(family="tabicl")
    .int("n_estimators", 1, 32, log=True)
    .float("softmax_temperature", 0.5, 2.0)
    .categorical("average_logits", [True, False])
    .categorical("norm_methods", ["none", "power", "quantile"])
)

hpo_space = op.HPOSpace().add(tabm).add(tabicl)
model = op.reasoning.fit(
    train_df, 
	hpo_space=hpo_space,
	optimizer=op.Optimizer(kind="tpe", n_trials_per_step=5, n_steps=4),
	task=op.Regression(label_column="sales_30d"),
).wait()  

Getting Started

Concepts

Data Connectors

Optimized Reasoning Models At Enterprise Scale

​Optimized Reasoning Models At Enterprise Scale

Optimized Reasoning Models At Enterprise Scale