LLMs as constraint solvers. Fine-tuning adds constraints to an existing solution. Gentle = small steps near the current solution. Coherent = new constraints consistent with existing ones. Diversity is a COHERENCE mechanism — forces the solver to satisfy all constraints simultaneously. Over-training = one constraint dominating = solver drops competing constraints. Predictions for training behavior grounded in this framework. |
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| .. | ||
| checkpoint | ||
| research | ||
| apollo_mini.py | ||
| apollo_worker.py | ||
| DESIGN.md | ||
| export_weights.py | ||
| extract_steering_vector.py | ||
| first_training_step.py | ||
| start_vllm_with_apollo.sh | ||
| train.py | ||
| training_example.py | ||
| vllm_export_hook.py | ||
| weight_mapping.py | ||