Reinforcement Learning Simulation

The BMW Assembly Line Approach

What you described: simulating 10 years of configurations in days to find optimal setup.

How It Works

1. Define objective function (what does 'good' mean?)
2. Create simulation environment (virtual testbed)
3. Agent takes actions (model generates responses)
4. Environment gives reward (score the response)
5. Agent updates (learn from feedback)
6. Repeat millions of times

Challenges for LARS

The Reward Problem

For BMW: Efficiency is measurable (throughput, cost, time) For LARS: What makes a 'good' response? - Accuracy? (need ground truth) - Helpfulness? (subjective) - Nexus alignment? (need to define)

Possible Solutions

1. Human feedback loop (RLHF) - expensive but effective
2. AI judge - use Claude to score LARS responses
3. Task completion - did LARS accomplish measurable goals?
4. Consistency - does LARS give stable, non-contradictory answers?

Simulation Environment for LARS

Simulator:
  - Virtual Nexus environment
  - Fake user queries
  - Task completion scoring
  - Response quality metrics

Loop:
  1. Generate query
  2. LARS responds
  3. Score response (AI judge + rules)
  4. Update weights via RL
  5. Repeat

Reality Check

• RL is compute-intensive (more than supervised learning)
• Reward design is hard (garbage in, garbage out)
• But: can find solutions humans wouldn't think of
• The 72GB VRAM will help for larger RL runs