Interestingly enough we have found the same net result -- structural guardrails are the unlock for smaller models. Our approach in particular layers three things: a parse rescue for malformed/incorrect tool calls (similar to your retry nudges), content-level intervention (diff size rejection, checkpoint forcing) and state machine enforcement on top (per-phase tool restriction, transition guards). On 13B models we saw completion of a selection of SWE-bench tasks went from ~20% to 100%. With frontier models we saw a reduction in API calls from reduced thrashing.
One of the most surprising findings was when a 9B model self-corrected through 4 tool parse failures within the guard rails. It tried to use a complex tool (patch_file), kept failing and eventually downshifted to a simpler tool (edit_line) that it could actually execute. The guardrails didn't make the model smarter, it just narrowed the execution space until it could find something that worked.
One of the most surprising findings was when a 9B model self-corrected through 4 tool parse failures within the guard rails. It tried to use a complex tool (patch_file), kept failing and eventually downshifted to a simpler tool (edit_line) that it could actually execute. The guardrails didn't make the model smarter, it just narrowed the execution space until it could find something that worked.
Brief: https://statewright.ai/research