Learn the next task. Keep a path back.
New training can change skills a model already has. Akasha’s Rust framework gives developers retention strategies and checkpoint orchestration to integrate with their own models. Evaluate each update against the work that still matters.
Continual learning
New skills. Earlier progress.
Make the next update a deliberate choice.
This candidate misroutes an earlier question. Keep the checkpoint while you refine retention and replay, then evaluate again.
Example evaluation workflow using fictional support tasks.
Make room for new skills.
A support model learns a new request category while earlier categories remain part of its job. Combine retention methods with examples from both task sets to assess what changed.
- 01Choose what to protectChoose the model and tasks you plan to train. Establish a baseline for the behavior the next update should retain.
- 02Preserve useful behaviorElastic Weight Consolidation, or EWC, estimates which model weights mattered to an earlier task and discourages large changes to them during later learning.
- 03Track what contributesSynaptic intelligence measures which parameters contribute during training. Those importance scores provide another way to protect prior learning.
- 04Revisit earlier examplesReplay mixes previous experience with new training. Generative replay reconstructs earlier features so new work can be learned alongside reminders of past tasks.
- 05Save a checkpointIntegrate the Rust checkpoint orchestrator with your model components and persistence backend. Save a starting point before the next update.
- 06Restore when neededConnect component recovery to your training workflow, then test restoring the saved state before relying on it during an update.
Choose a retention strategy for your model.
Enable Elastic Weight Consolidation and configure the replay buffer in the Rust framework. Your training integration supplies the model, examples, and evaluation process.
Explore the learning illustration
use akasha_continual::ContinualConfig;
let mut config = ContinualConfig::default();
config.strategies.ewc.enabled = true;
config.strategies.ewc.fisher_samples = 1_000;
config.buffer.capacity = 1_000;
config.buffer.priority_replay.enabled = true;
// Integrate the configuration with your model and training loop.
// Evaluate both earlier tasks and new tasks after each update.Protection and recovery, with explicit controls.
Integrate retention methods into your training workflow. The Rust checkpoint orchestrator provides interfaces for registering components, saving state, and requesting recovery through a configured persistence backend.
- EWC
- discourage changes to important weights
- Synaptic intelligence
- track parameter importance during training
- Replay
- revisit previous experience during new learning
- Progressive networks
- preserve earlier columns while adding task capacity
- EWC configuration
- enabled · fisher_samples · lambda
- Orchestrator
- CheckpointOrchestrator
- Components
- CheckpointableComponent implementations
- Storage
- Configured PersistenceBackend
- Checkpoint request
- create_checkpoint(trigger, components)
- Recovery request
- start_recovery(checkpoint_id, components)
- Observation
- Status and checkpoint events
Keep a foundation for the next task.
Manage how your system learns, preserve useful progress, and save a state you can return to.