# MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning

Paper: [arXiv:2603.09892v1](https://arxiv.org/abs/2603.09892v1)

## Core Idea

This paper studies catastrophic forgetting during continual fine-tuning and proposes an adaptive replay method that estimates how well examples are retained, then schedules rehearsal accordingly.

## Why It Matters Here

Not all past samples are equally worth revisiting.

That maps cleanly to debugging and observability:

- not all trace events are equally worth storing forever
- not all sessions are equally worth replaying
- not all historical runs deserve the same retrieval priority

## Key Takeaways For The Repo

### 1. Importance should evolve over time

The current repo already has an `importance` score. This paper suggests that importance should not be purely static.

A better system would consider:

- novelty
- failure recurrence
- later reuse value
- whether the event belongs to a fragile or frequently failing workflow

### 2. Replay should be adaptive

If this repo grows into a debugging memory system, replay should not be "latest first" only. It should elevate:

- representative failures
- rare but high-cost traces
- regressions that reappear
- sessions tied to previously fixed bugs

### 3. Storage policy should be smarter than FIFO

The same logic can improve retention:

- keep compact summaries for routine sessions
- preserve full detail for high-value sessions
- downsample low-value traces
- keep checkpoints where recovery or comparison value is highest

## Concrete Opportunities

- replace static importance with a composite score
- add retention tiers for traces and checkpoints
- surface "high replay value" sessions in the UI
- cluster repeated failures and keep representative traces

## Caution

The paper is about continual model training, not directly about a debugger product. The useful transfer is the retention and replay mindset, not the exact algorithm.

## Best Next Experiment

Replace the current single importance score with a simple composite ranking:

- base event importance
- failure severity
- rarity
- session reuse value

Then use that ranking in one place first, such as session ordering or checkpoint retention.
