Two additional penalties address degenerate behaviors. A repeated pruning penalty, , of 0.1 per excess call is applied to consecutive prune streaks longer than 3 (capped at 0.5), discouraging the agent from pruning one chunk at a time across many turns rather than batching. A turn count penalty, , increases linearly from 0 at 64 turns to 0.5 at 128 turns, discouraging trajectories with diminishing-return searches. The final reward is floored at for any trajectory that completes without error and capped at the pre-penalty value, , ensuring that successful trajectories always dominate failed ones while preventing the floor from inflating penalized rewards.
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The combined approach achieves 3.5 bits per channel with "absolute quality neutrality" across Gemma, Mistral, and Llama-3.1-8B-Instruct, validated across LongBench, Needle In A Haystack, ZeroSCROLLS, RULER, and L-Eval. At 2.5 bits, accuracy degradation remains minimal. The headline achievement: 6x KV memory reduction without measurable accuracy loss, with 4-bit TurboQuant delivering 8x performance improvement over 32-bit unquantized keys on H100 GPUs.
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console.print(f"\n[bold] Launching template:[/bold] {tmpl['name']}")