Model modifications

Train weights, edit knowledge, or work with activations. Explore how each method changes a model.

Train a LoRA adapter on reviewed answers while keeping base weights frozen.

Reviewed examplesInputs and approved answers

Supervised trainingLearn from target-token loss

Trained adapterSave with its exact base model

Support conversation

Frozen base model

Train LoRA weights

Your answer format

Added adapter weights

ExampleTeach a support model to return your response format from reviewed conversations.

Ponder Training

Train, evaluate and improve a model from the same experiment setup.

SFTContinual learning: Supervised fine-tuning teaches the model from reviewed examples. Save a trained adapter.
RLContinual learning: Reinforcement learning with GRPO scores groups of responses and learns from the rewards.
Continual learning: Schedule reviewed SFT batches or compatible GRPO updates, with review, spending limits and independent evaluation.

Training loopLoRA adapter

New outcomes. Next adapter.

Keep learning from the work your agents already do. Bring in new tasks, add feedback, and measure whether each update makes your model better.

Conversation logs: Connect your agents and choose which conversations to import.
Your harness: Send tasks and results through the OpenPond CLI or SDK.
Fix-it AgentBeta: Collect supported tool failures and recovery evidence for review.
Manual tasks: Add examples yourself, or import a dataset you already have.

Work with agents beside your local projects, then turn a dataset into an evaluation plan without leaving the app.