stability(queen): Long-term training stability study (10000+ episodes)

[gHashTag]

Issue from Trinity Improvement Plan (Part 1, Priority 1)

Context

The Queen self-learning system has only been tested for 847 episodes. Long-term stability is unknown.

Task Description

Run Queen for 10,000+ episodes to verify training stability and identify failure modes.

Requirements

1. Duration: 10,000+ episodes

2. Metrics to track:

   • Loss drift over time
   • Convergence patterns
   • Resource usage (memory, CPU)
   • Episode success rate
   • Reward distribution

3. Failure modes to document:

   • Divergence patterns
   • Memory leaks
   • Recovery strategies
   • Crash conditions

4. Monitoring:

   • Logging every 100 episodes
   • Checkpoint every 1000 episodes
   • Resource usage snapshots

Deliverables

1. Long-term stability report
2. Failure mode documentation
3. Recovery procedure guide
4. Updated README with stability claims

Timeline

• 1-2 weeks for automated run
• 1 week for analysis
• Total: 3 weeks

Success Criteria

• Queen trains for 10,000+ episodes without divergence
• <5% episode failure rate after convergence
• Resource usage remains bounded (<2GB memory)
• Documented recovery procedures

Labels

priority: high, stability, research, queen
type: experiment
component: queen, hslm

[gHashTag]

Requires GPU training infrastructure. Not actionable without hardware access.