SteerDistill preserves 4-bit agent reasoning

Quantizing agents has always been bottlenecked by compounding multi-step errors, where a single missed environmental detail derails an entire rollout. SteerDistill provides the missing link: pinpointing specific failure turns during distillation rather than paying the massive compute overhead of reinforcement learning.

• –Targeted intervention over brute force: Instead of rolling out hundreds of costly RL trajectories like QeRL, a frozen BF16 teacher iteratively nudges the student only when execution checks fail.
• –True offline autonomy: Because verified corrections are distilled directly into model weights via context distillation, deployment requires zero teacher overhead or runtime steering prompts.
• –Closes the 4-bit gap: Across benchmarks like BFCL V4 and WebShop, W4A16 models score within one point of unquantized BF16 baselines across Gemma and Qwen architectures.
• –Big win for edge and local agents: Slashing memory footprints to 4-bit without sacrificing multi-turn tool use makes complex agentic workflows viable on consumer GPUs and edge hardware.