Dev

configs/config.yaml

@@ -12,7 +12,7 @@ data:
 
 collab:
   mode: TAKE_JOB           # ONE | TAKE_JOB
-  num_agents: 3       # used when mode=TAKE_JOB
+  num_agents: 2       # used when mode=TAKE_JOB
 
 external:
   mode: code_feedback   # plain | plain_simple | code_feedback
@@ -24,15 +24,15 @@ trainer:
   num_train_epochs: 3
   per_device_train_batch_size: 1
   # Learning rate for optimizer (alias: lr)
-  learning_rate: 1.7e-5
+  learning_rate: 1e-5
   logging_steps: 50
   save_steps: 200
   num_generations: 3
   # Per-agent generation cap; increase if outputs truncate.
-  max_new_tokens: 660
+  max_new_tokens: 600
   temperature: 0.25
   top_p: 0.90
-  num_turns: 2
+  num_turns: 1
   # PPO-related (CoMLRL MAGRPO) options
   # Whether to normalize advantages when updating policy
   # normalize_advantage: true

rewards/CE_reward.py

@@ -394,20 +394,7 @@ def collect_calls(fn: "ast.FunctionDef") -> Set[str]:
 _count_pass_syntax = 0
 
 def get_reward_function(strategy, num_agents: int) -> Callable[..., List[float]]:
-    """Return a reward function implementing the redesigned lv1+lv2+lv3 scoring.
-
-    - V = total number of class methods requiring implementation
-    - lv1 = 2 * (|union of chosen methods across agents| / V)
-      Special case: if coverage < 1/2 then reward = 0 for this sample
-    - lv2 = total-picks control:
-      Let S = Σ_i |A_i| be the total number of functions generated by all agents.
-        * If S ≥ 2V+2: terminate this sample early with total reward = -INF (=-1)
-        * If 0 <= S <= V: lv2(S) = 2 - 3 * ((S - V)^2) / V^2 (assuming V>0)
-        * If V < S <= 2V+2: lv2(S) = 2 - 3 * ((S - V)^2) / (V + 1)^2
-    - lv3 = balance based on variance of |A_i| around t = V/N, with
-      MSD = (1/N) * Σ (s_i - t)^2 and MSD_max = (1/N) * V^2 * (1 - 1/N),
-      R_bal = max(0, 1 - MSD/(MSD_max + eps))
-    Total reward = lv1 + lv2 + lv3
+    """Return a reward function
     """
 
     def reward_wrapper(*agent_completions, batch_items=None, prompts=None):
@@ -447,9 +434,12 @@ def reward_wrapper(*agent_completions, batch_items=None, prompts=None):
             INF = 1
             _count_total += 1
 
+            V_set: Set[str] = set(method_names)
+            V = len(V_set)
+
             # Early penalty: penalize by number of agents with zero functions (k * -INF) and skip
             try:
-                zeros = sum(1 for s in A_sets if (len(s) if s is not None else 0) == 0)
+                zeros = sum(1 for s in A_sets if (len(s) if s is not None else 0) in (0, V))
                 if zeros > 0:
                     rewards.append(-INF * 0.5 * zeros)
                     continue
@@ -460,8 +450,7 @@ def reward_wrapper(*agent_completions, batch_items=None, prompts=None):
             _count_pass_lv0 += 1
 
             # New reward rules (lv1 + lv2)
-            V_set: Set[str] = set(method_names)
-            V = len(V_set)
+
             if V <= 0:
                 rewards.append(-INF)
                 continue
@@ -477,11 +466,13 @@ def reward_wrapper(*agent_completions, batch_items=None, prompts=None):
                 continue
 
             lv1 = 2.0 * coverage_ratio
+            if coverage_ratio == 1.0:
+                lv1 += 0.5  # bonus for full coverage
 
             # lv2: constrain total picks S = sum_i |A_i|
             S_total = sum(len(s) for s in A_sets)
             # Early termination if total picks exceed 2V
-            if S_total > 2 * V + 2:
+            if S_total >= 2 * V:
                 rewards.append(-INF)
                 continue
 
@@ -522,7 +513,7 @@ def reward_wrapper(*agent_completions, batch_items=None, prompts=None):
                         sum_J += J
                         N_pairs += 1
                 mean_J = (sum_J / N_pairs) if N_pairs > 0 else 0.0
-                jaccard_term = 1.0 * (1.0 - 1.5 * mean_J)
+                jaccard_term = 1.0 * (1.0 - 2.0 * mean_J)
                 # if jaccard_term > 2.0:
                 #     jaccard_term = 2.0
                 # elif jaccard_term < -2.0:

utils/prompting.py

@@ -81,6 +81,12 @@ def build_take_job_prompt(
         SKELETON START
         {skeleton.strip()}
         SKELETON END
+
+        As a final reminder, please select a **non-empty, proper subset** of {v_braced} to implement.
+
+        We recommend choosing a consecutive block of methods that either starts at the beginning of {v_braced} or ends at its last method (DO NOT limit yourself to only the beginning).
+
+        Take particular care not to select all methods for implementation!
         """
     ).strip()