Module training Future

Training Module - Future Enhancements

Scope

• hardening and refinement of training runtime behavior
• deterministic reliability improvements for dataset/training/adapter paths
• stronger benchmark-backed guardrails for training hot paths

Design Constraints

• training contracts remain backward compatible within major release line.
• labeling and adapter lifecycle outcomes remain explicit and deterministic.
• degraded checkpoint and enrichment paths remain observable and non-silent.
• acceleration-dependent training behavior remains bounded and diagnosable.

Required Interfaces

Interface	Requirement
dataset interfaces	deterministic labeling and enrichment behavior
training interfaces	stable LoRA/AdaLoRA lifecycle semantics
checkpoint interfaces	explicit save/resume/verify behavior
adapter interfaces	bounded merge and serving-handoff behavior

Implementation Notes

• tighten parity between checkpoint integrity behavior and training diagnostics.
• standardize incident taxonomy for labeling, enrichment, and serving classes.
• expand resilience tests for prolonged adapter lifecycle workloads.
• broaden benchmark depth for training-pipeline and checkpoint scenarios.

Wave B B3: Multi-Task LoRA Training Enablement

Priority: High Target: Q1–Q2 2027

• add shared LoRA base training path with task-specific projection handling
• add domain-gating support for task-conditional adapter usage during training
• add joint multi-task loss pipeline with configurable task weighting
• add ablation workflows for shared-base vs separate-adapter comparisons
• run three-task transfer/robustness benchmarks for Wave B acceptance tracking

Test Strategy

• unit and integration suites for labeling, training, and adapter lifecycle behavior.
• regressions for checkpoint corruption, merge conflicts, and rollback edge cases.
• deterministic stress runs for training and adapter-serving handoff workloads.
• release-profile benchmark runs for mapped training targets.

Performance Targets

• training hot paths remain inside regression budgets.
• adapter lifecycle and training-step-sensitive operations remain stable at p95/p99 envelopes.
• mapped benchmark manifests reach no-missing-case status for release gating.

Security / Reliability

• maintain strict bounded behavior for training and checkpoint transitions.
• preserve explicit failure signaling for labeling, adapter, and serving faults.
• enforce predictable degradation under capability or runtime pressure.
• keep diagnostics actionable for production training incidents.

Planning Traceability

• Wave B dependency planning issue: #5039
• Upstream planning context: Wave C #5040, Wave A #5038

Wave B Acceptance Gates (B3)

• average task performance gain ≥ +8% vs single-task baseline
• training-time increase ≤ 15% across benchmarked multi-task workloads
• robust convergence and stability across configured task-weight schedules

Related Documents

• AI wave tracker: ../ai/ROADMAP.md
• LLM wave tracker: ../llm/ROADMAP.md
• bibliography: ../../docs/research/ml_enhancements_bibliography.md
• issue scope: https://github.com/makr-code/ThemisDB/issues/5039

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