dont cache interesting buffer-based datas

hypothesis-python/src/hypothesis/internal/conjecture/engine.py

@@ -357,8 +357,25 @@ def _cache(self, data: Union[ConjectureData, ConjectureResult]) -> None:
         # write to the buffer cache here as we move more things to the ir cache.
         if data.invalid_at is None:
             self.__data_cache[data.buffer] = result
-        key = self._cache_key_ir(data=data)
-        self.__data_cache_ir[key] = result
+
+        # interesting buffer-based data can mislead the shrinker if we cache them.
+        #
+        #   @given(st.integers())
+        #   def f(n):
+        #     assert n < 100
+        #
+        # may generate two counterexamples, n=101 and n=m > 101, in that order,
+        # where the buffer corresponding to n is large due to eg failed probes.
+        # We shrink m and eventually try n=101, but it is cached to a large buffer
+        # and so the best we can do is n=102, a non-ideal shrink.
+        #
+        # We can cache ir-based buffers fine, which always correspond to the
+        # smallest buffer via forced=. The overhead here is small because almost
+        # all interesting data are ir-based via the shrinker (and that overhead
+        # will tend towards zero as we move generation to the ir).
+        if data.ir_tree_nodes is not None or data.status < Status.INTERESTING:
+            key = self._cache_key_ir(data=data)
+            self.__data_cache_ir[key] = result
 
     def cached_test_function_ir(
         self, nodes: List[IRNode]
@@ -1218,7 +1235,7 @@ def shrink_interesting_examples(self) -> None:
             self.interesting_examples.values(), key=lambda d: sort_key(d.buffer)
         ):
             assert prev_data.status == Status.INTERESTING
-            data = self.new_conjecture_data_for_buffer(prev_data.buffer)
+            data = self.new_conjecture_data_ir(prev_data.examples.ir_tree_nodes)
             self.test_function(data)
             if data.status != Status.INTERESTING:
                 self.exit_with(ExitReason.flaky)