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Add "Nailed it" rating — prompt-adherence positive, weaker than Perfect

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Restores the old "Loved it" learning behavior (reward 0.75, intent delta
+0.03, preferred context 0.65, axis conditioning 0.35, LoRA 0.44/0.24)
as a distinct rating. Sits between Perfect and Loved it on the ladder:
prompt adherence is rewarded but not at full strength.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
2026-05-30 11:19:44 +03:00
co-authored by Claude Sonnet 4.6
parent aa47dca72b
commit 24640a02bf
1 changed files with 33 additions and 8 deletions
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conditioning.py
+33 -8
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@@ -5633,6 +5633,7 @@ _V2_PERSISTENT_CACHE_MAX = 4096
V2_RATING_LABELS = [
"-Just forget it-",
"Perfect",
"Nailed it",
"Loved it",
"Missing details",
"Missing action",
@@ -5651,6 +5652,7 @@ V2_RATING_PROFILES = {
"-Just forget it-": {"key": "forget", "reward": 0.0, "level": 0, "missing_axes": [], "skip_learning": True},
"Initial discovery": {"key": "discover", "reward": 0.0, "level": 4, "missing_axes": []},
"Perfect": {"key": "like", "reward": 1.0, "level": 8, "missing_axes": []},
"Nailed it": {"key": "nailed_it", "reward": 0.75, "level": 7, "missing_axes": []},
"Loved it": {"key": "loved_it", "reward": 0.85, "level": 7, "missing_axes": []},
"Missing details": {"key": "missing_details", "reward": 0.35, "level": 6, "missing_axes": ["details"]},
"Missing action": {"key": "missing_action", "reward": 0.05, "level": 5, "missing_axes": ["action"]},
@@ -6467,7 +6469,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
return "Seed memory: seed output not connected."
if not isinstance(last_run, dict) or rating_profile.get("skip_learning"):
return "Seed memory: no previous sampler seed to learn from."
if rating_profile.get("key") not in {"like", "loved_it"}:
if rating_profile.get("key") not in {"like", "loved_it", "nailed_it"}:
return "Seed memory: rating not successful."
if not last_run.get("seed_output_connected", seed_output_connected):
return "Seed memory: previous seed output was not connected."
@@ -7285,7 +7287,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
wrong_axes = set(rating_profile.get("wrong_axes", [])) & all_axes
if key == "awful":
missing_axes = set(all_axes)
elif key in {"like", "loved_it"}:
elif key in {"like", "loved_it", "nailed_it"}:
missing_axes = set()
wrong_axes = set()
@@ -7324,7 +7326,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
return axis_feedback, "Repair persistence: unavailable."
active = set(global_state.get("active_repair_axes", []) or []) & set(V2_FEEDBACK_AXES)
key = learning_profile.get("key", "") if isinstance(learning_profile, dict) else ""
if key in {"like", "loved_it"}:
if key in {"like", "loved_it", "nailed_it"}:
active = set()
global_state["active_repair_axes"] = []
feedback = dict(axis_feedback or {})
@@ -8151,12 +8153,14 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
entry["phrases"] = phrases_for_token[-12:]
if token in intent_token_set:
entry["intent_count"] = int(entry.get("intent_count", 0)) + 1
delta = 0.06 if rating_key == "like" else 0.0
delta = 0.06 if rating_key == "like" else (0.03 if rating_key == "nailed_it" else 0.0)
else:
entry["enhancer_only_count"] = int(entry.get("enhancer_only_count", 0)) + 1
if rating_key == "like":
delta = 0.12
entry["accepted_count"] = int(entry.get("accepted_count", 0)) + 1
elif rating_key == "nailed_it":
delta = 0.07
elif rating_key == "loved_it":
delta = 0.07
elif phrase_is_bad_extra:
@@ -8201,6 +8205,9 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
if rating_key == "like":
delta = -0.18
entry["forgiven_count"] = int(entry.get("forgiven_count", 0)) + 1
elif rating_key == "nailed_it":
delta = -0.10
entry["forgiven_count"] = int(entry.get("forgiven_count", 0)) + 1
elif rating_key == "loved_it":
continue # axis-blind quality endorsement — don't learn prompt adherence
elif rating_key == "awful":
@@ -8409,6 +8416,9 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
if rating_profile.get("key") == "like":
positive_axes = {"action", "details"}
base_delta = 1.0
elif rating_profile.get("key") == "nailed_it":
positive_axes = {"action", "details"}
base_delta = 0.65
elif rating_profile.get("key") == "loved_it":
return "Preferred context: skipped for axis-blind quality endorsement."
else:
@@ -8600,6 +8610,18 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
else:
delta = 0.0
count_category_evidence = False
elif rating_profile.get("key") == "nailed_it":
delta = 0.35 * kind_scale
entry["liked_count"] = int(entry.get("liked_count", 0)) + 1
rating_evidence["liked"] = int(rating_evidence.get("liked", 0)) + 1
entry["satisfied_count"] = int(entry.get("satisfied_count", 0)) + 1
satisfied_axis_counts = entry.setdefault("satisfied_axes", {})
for axis in V2_FEEDBACK_AXES:
satisfied_axis_counts[axis] = int(satisfied_axis_counts.get(axis, 0)) + 1
bump_evidence("satisfied_axes", V2_FEEDBACK_AXES)
for category, score in effective_before.items():
if float(score) >= 0.28:
weights[category] = float(weights.get(category, 0.0)) + 0.08 * kind_scale
elif rating_profile.get("key") == "loved_it":
delta = 0.35 * kind_scale
entry["liked_count"] = int(entry.get("liked_count", 0)) + 1
@@ -9062,7 +9084,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
)
self._v2_update_axis_conditioning_memory(global_state, payload, axis_feedback,
session_mean_payload=session_mean)
if key in {"like", "loved_it"}:
if key in {"like", "loved_it", "nailed_it"}:
liked_dir_slot = global_state.setdefault("liked_dir", {})
self._v2_store_direction(liked_dir_slot, payload, session_mean)
count = int(global_state.get("liked_conditioning_count", 0))
@@ -9095,7 +9117,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
avg = float(global_state.get("avg_reward_ema", 0.0))
avg = 0.86 * avg + 0.14 * reward
global_state["avg_reward_ema"] = round(avg, 6)
if rating_profile.get("key") in {"like", "loved_it"}:
if rating_profile.get("key") in {"like", "loved_it", "nailed_it"}:
global_state["good_streak"] = int(global_state.get("good_streak", 0)) + 1
global_state["bad_streak"] = 0
elif reward < -0.25:
@@ -10706,7 +10728,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
profile = V2_RATING_PROFILES.get(rating_label) or V2_RATING_PROFILES.get(V2_RATING_ALIASES.get(str(rating_label), ""), {})
rating_key = profile.get("key", "")
appearance_wrong = rating_key == "wrong_appearance"
positive = rating_key in {"like", "loved_it"}
positive = rating_key in {"like", "loved_it", "nailed_it"}
negative = float(profile.get("reward", 0.5)) < 0.3
lines = []
for ch in diff:
@@ -11015,6 +11037,9 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
lora_axes = self._v2_lora_feedback_axes(lora_type)
if rating_profile.get("key") == "like":
offset += step * max(0.18, relation) * 0.55
elif rating_profile.get("key") == "nailed_it":
quality_type = lora_type in {"quality", "style"}
offset += step * max(0.18, relation) * (0.44 if quality_type else 0.24)
elif rating_profile.get("key") == "loved_it":
quality_type = lora_type in {"quality", "style"}
offset += step * max(0.18, relation) * (0.55 if quality_type else 0.30)
@@ -11772,7 +11797,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
from ltx_enhancements import build_enhancements, bless_attention_maps
# Bless attention maps when the previous run was rated Perfect or Loved it
if eff_key and isinstance(learning_profile, dict) and learning_profile.get("key") in {"like", "loved_it"}:
if eff_key and isinstance(learning_profile, dict) and learning_profile.get("key") in {"like", "loved_it", "nailed_it"}:
bless_attention_maps(eff_key)
patched_model = build_enhancements(