RFD4-Proteína Reward-Guided Search

Purpose

Reward requirements

Complete rfd4-proteina-setup first. Generation requires a CUDA GPU. Rewards listing refolding oracles also require a configured refolding backend; RFD4-Proteína supports AF2, AF3, and OpenFold3 through metrics/refolding_oracles. Rewards such as interchain_interactions do not require a refolding oracle.

Search explores multiple generation trajectories and keeps the candidates a reward scores highest. Beam search ships ready to run: it branches and prunes partial trajectories at step checkpoints, scoring survivors with a reward model. The shipped inference_beam_example uses the default AF3 oracle for refolding confidence; other search configs may select another backend or avoid refolding entirely.

Search runs through the same entry point as generation, generate.py, selected by an inference_experiment config that adds one search: block. All arguments are Hydra key=value (never --key value).

Run everything from the repository root (the directory containing .project-root).

Prerequisites

A working generation runtime, validated design inputs, and the dependencies of each selected reward provider.

Use the user-selected model checkout at RFPROTEINA_ROOT for model commands, configs, and upstream manuals. This catalog contains the skills, not that checkout. In helper examples, resolve open-models-skills/rfd4-proteina/<name>/... to its absolute installed path when the catalog and model checkout are separate.

Inputs

Required: the user's inputs, output directory, reward objective, and compute budget. Optional: checkpoint, beam, branch, and reward-weight overrides. Read other defaults from the chosen config and preserve selected scheduler resources.

Instructions

Step 1, Preflight the selected reward dependencies

Every search needs a green rfd4-proteina-setup: