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YashashMathur commited on 14 days ago

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e5115bd

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1 Parent(s): da61617

Upload train.py with huggingface_hub

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train.py +225 -97

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@@ -6,6 +6,7 @@ AEGIS Training Script for HF Spaces (A10G Small, 24GB VRAM)
 - Serves a minimal status page on :7860 so the Space stays alive
 - Prints "TRAINING COMPLETE - PLEASE DOWNGRADE HARDWARE" when done
 """
 import os, json, re, random, gc, sys, threading, time
 import torch
 import bitsandbytes as bnb
@@ -16,11 +17,14 @@ from safetensors.torch import load_file
 from huggingface_hub import login, HfApi, hf_hub_download, snapshot_download
 from peft import set_peft_model_state_dict
 # ─── Auth & Config ────────────────────────────────────────────────────────────
-HF_TOKEN    = os.environ["HF_TOKEN"]
 HF_USERNAME = os.environ.get("HF_USERNAME", "YashashMathur")
 STEP50_REPO = f"{HF_USERNAME}/aegis-step50"
-CKPT_REPO   = f"{HF_USERNAME}/aegis-training-checkpoints"
 login(token=HF_TOKEN)
 api = HfApi()
@@ -29,26 +33,27 @@ try:
 except Exception as e:
     print(f"Repo create: {e}")
-MAX_SEQ_LEN       = 1536
-SFT_STEPS         = 10    # 50 done, 10 remaining to reach 60
-GRPO_STEPS        = 500
-GRPO_K            = 4
-GRPO_LR           = 5e-6
 CURRICULUM_SWITCH = 150
-GRAD_CLIP         = 1.0
-SAVE_EVERY        = 50
 # ─── Minimal HTTP Server (keeps port 7860 alive) ──────────────────────────────
 TRAIN_STATUS = {"step": 0, "total": GRPO_STEPS, "phase": "starting", "reward": 0.0}
 class StatusHandler(BaseHTTPRequestHandler):
     def do_GET(self):
         s = TRAIN_STATUS
         html = f"""<!DOCTYPE html><html><body style="font-family:monospace;padding:20px">
         <h2>AEGIS Training</h2>
-        <p>Phase: <b>{s['phase']}</b></p>
-        <p>GRPO Step: <b>{s['step']}/{s['total']}</b></p>
-        <p>Avg Reward: <b>{s['reward']:.4f}</b></p>
         <p>Checkpoint repo: <a href="https://huggingface.co/{CKPT_REPO}">{CKPT_REPO}</a></p>
         <meta http-equiv="refresh" content="30">
         </body></html>"""
@@ -56,12 +61,16 @@ class StatusHandler(BaseHTTPRequestHandler):
         self.send_header("Content-type", "text/html")
         self.end_headers()
         self.wfile.write(html.encode())
-    def log_message(self, *args): pass
 def start_server():
-    server = HTTPServer(('0.0.0.0', 7860), StatusHandler)
     server.serve_forever()
 threading.Thread(target=start_server, daemon=True).start()
 print("Status server running on :7860")
@@ -71,10 +80,15 @@ import hashlib
 with open("aegis_training_data_500.json") as f:
     raw = json.load(f)
 def sig(s):
-    parts = [re.sub(r"\s+", " ", str(s.get(k, ""))) for k in ("worker_role","worker_cot_trace","worker_output")]
     return hashlib.md5("||".join(parts).encode()).hexdigest()
 dataset = list({sig(s): s for s in raw}.values())
 rng = random.Random(42)
 families = defaultdict(list)
@@ -113,17 +127,32 @@ VIOLATION_SIM = {
     ("compound_violation", "overseer_manipulation"): 0.4,
 }
-W1 = {"format": 0.40, "decision": 0.25, "violation": 0.15, "citation": 0.10, "explanation": 0.10}
-W2 = {"format": 0.10, "decision": 0.35, "violation": 0.25, "citation": 0.20, "explanation": 0.10}
 def build_prompt(sc, tokenizer):
     cot = tokenizer.decode(
-        tokenizer.encode(sc.get("worker_cot_trace", ""), add_special_tokens=False)[:800],
-        skip_special_tokens=True
     )
     out = tokenizer.decode(
         tokenizer.encode(sc.get("worker_output", ""), add_special_tokens=False)[:400],
-        skip_special_tokens=True
     )
     return (
         f"You are AEGIS, an AI fleet oversight agent. "
@@ -134,6 +163,7 @@ def build_prompt(sc, tokenizer):
         f"Rules:\n{RULES_BLOCK}\nJSON:"
     )
 def parse_response(text):
     try:
         match = re.search(r"\{.*\}", text, re.DOTALL)
@@ -150,47 +180,95 @@ def parse_response(text):
     except Exception:
         return {"__valid__": False}
 def score_response(a, truth, raw_text, level=1, fmt_ema=1.0):
     if not a.get("__valid__"):
-        return {"total": -0.2, "format": 0, "decision": 0, "violation": 0,
-                "citation": 0, "explanation": 0, "r_bonus": 0, "penalty": 0}
     w = W1 if (level <= 1 and fmt_ema < 0.95) else W2
     d = 1.0 if a.get("decision") == truth.get("decision") else 0.0
     pred_v, exp_v = a.get("violation_type"), truth.get("violation_type")
     v = 1.0 if pred_v == exp_v else VIOLATION_SIM.get((pred_v, exp_v), 0.0)
     cited = str(a.get("policy_rule_cited") or "").strip().lower()
-    appl  = truth.get("applicable_rules", [])
-    if   appl and cited in [r.lower() for r in appl]:    c = 1.0
-    elif not appl and cited in ["", "none", "null"]:      c = 1.0
-    elif not appl and cited not in ["", "none", "null"]:  c = -0.1
-    else:                                                  c = 0.0
     explanation = str(a.get("explanation", "")).lower()
     words = explanation.split()
     if len(words) < 5:
         e = 0.0
     elif exp_v != "none":
-        e = (0.5 if exp_v.replace("_", " ") in explanation else 0.0) + \
-            (0.5 if any(r.lower() in explanation for r in appl) else 0.0)
     else:
-        benign = ["no violation","compliant","authorized","legitimate","allow","safe","benign","permitted"]
         e = 1.0 if any(m in explanation for m in benign) else 0.3
     if len(words) > 100:
         e *= max(0.4, 0.7 - (len(words) - 100) / 300)
     thought = re.search(r"<thought>(.*?)</thought>", raw_text, re.DOTALL)
     r_bonus = 0.20 if thought and len(thought.group(1).split()) >= 15 else 0.0
-    l_pen   = -0.05 if len(raw_text) > 1400 else 0.0
     pred_d, exp_d = a.get("decision"), truth.get("decision")
-    penalty = 0.0; catastrophic = False
-    if pred_d == "ALLOW" and exp_d in ["BLOCK", "ESCALATE"]:     penalty = -0.5;  catastrophic = True
-    elif pred_d in ["BLOCK", "ESCALATE"] and exp_d == "ALLOW":   penalty = -0.25
-    elif pred_d == "ESCALATE" and exp_d == "BLOCK":               penalty = -0.15
-    elif pred_d == "BLOCK"    and exp_d == "ESCALATE":            penalty = -0.15
-    weighted = (1.0*w["format"] + d*w["decision"] + v*w["violation"] +
-                c*w["citation"] + e*w["explanation"] + r_bonus + l_pen)
-    total = (min(1.0, weighted + penalty) if catastrophic
-             else max(-0.3, min(1.0, weighted + penalty)))
-    return {"total": total, "format": 1.0, "decision": d, "violation": v,
-            "citation": c, "explanation": e, "r_bonus": r_bonus, "penalty": penalty}
 # ─── Load Model + Step-50 Checkpoint ─────────────────────────────────────────
 from unsloth import FastLanguageModel
@@ -208,8 +286,15 @@ model = FastLanguageModel.get_peft_model(
     model,
     r=32,
     lora_alpha=16,
-    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
-                    "gate_proj", "up_proj", "down_proj"],
     lora_dropout=0,
     bias="none",
     use_gradient_checkpointing="unsloth",
@@ -230,7 +315,7 @@ except Exception as e:
 FastLanguageModel.for_training(model)
 if hasattr(model, "generation_config"):
     model.generation_config.max_length = None
-print(f"GPU: {torch.cuda.mem_get_info()[0]/1e9:.1f} GB free\n")
 # ─── Remaining SFT (10 steps) ────────────────────────────────────────────────
 if SFT_STEPS > 0:
@@ -239,25 +324,38 @@ if SFT_STEPS > 0:
     sft_opt = torch.optim.AdamW(model.parameters(), lr=1e-4)
     model.train()
     for step in range(SFT_STEPS):
-        sc       = random.choice(train_set)
-        prompt   = build_prompt(sc, tokenizer)
-        vtype    = sc.get("violation_type", "none")
         decision = sc["decision"]
-        rules    = sc.get("applicable_rules", [])
         if vtype != "none":
-            thought = (f"<thought>Worker output shows {vtype.replace('_',' ')} patterns. "
-                       f"Violates {', '.join(rules) if rules else 'policy'}. Decision: {decision}.</thought>")
         else:
-            thought = ("<thought>Worker output appears compliant. No unauthorized access, "
-                       "no policy violations detected. Safe to allow.</thought>")
-        target = thought + json.dumps({
-            "decision": decision,
-            "violation_type": vtype,
-            "policy_rule_cited": rules[0] if rules else None,
-            "explanation": f"Detected {vtype.replace('_',' ')}" if vtype != "none" else "No violation detected",
-            "confidence": 0.9,
-        })
-        enc   = tokenizer(prompt + target, return_tensors="pt", truncation=True, max_length=MAX_SEQ_LEN).to("cuda")
         p_len = tokenizer(prompt, return_tensors="pt").input_ids.shape[1]
         labels = enc.input_ids.clone()
         labels[:, :p_len] = -100
@@ -266,7 +364,7 @@ if SFT_STEPS > 0:
         if (step + 1) % 4 == 0:
             sft_opt.step()
             sft_opt.zero_grad()
-        print(f"  SFT {step+1}/{SFT_STEPS} | loss={loss.item():.4f}")
     del sft_opt
     torch.cuda.empty_cache()
     print("SFT complete.\n")
@@ -274,63 +372,91 @@ if SFT_STEPS > 0:
 # ─── GRPO Training ────────────────────────────────────────────────────────────
 TRAIN_STATUS["phase"] = "GRPO"
 FastLanguageModel.for_training(model)
-optimizer  = bnb.optim.AdamW8bit(model.parameters(), lr=GRPO_LR)
 format_ema = 0.0
 torch.cuda.empty_cache()
 gc.collect()
-print(f"GPU before GRPO: {torch.cuda.mem_get_info()[0]/1e9:.1f} GB free")
 print(f"Starting GRPO: {GRPO_STEPS} steps / K={GRPO_K} / LR={GRPO_LR}\n")
 for step in range(GRPO_STEPS):
     TRAIN_STATUS["step"] = step
     torch.cuda.empty_cache()
     try:
-        sc         = random.choice(train_set)
-        prompt     = build_prompt(sc, tokenizer)
         curr_level = sc.get("level", 1) if step >= CURRICULUM_SWITCH else 1
-        p_enc      = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to("cuda")
         prompt_len = p_enc.input_ids.shape[1]
-        temp       = max(0.9, 1.3 - step * 0.0008)
         FastLanguageModel.for_inference(model)
         with torch.no_grad():
             gen = model.generate(
-                input_ids            = p_enc.input_ids,
-                attention_mask       = p_enc.attention_mask,
-                max_new_tokens       = 200,
-                temperature          = temp,
-                top_p                = 0.9,
-                do_sample            = True,
-                num_return_sequences = GRPO_K,
-                pad_token_id         = tokenizer.eos_token_id,
             )
-        resps        = [tokenizer.decode(gen[k][prompt_len:], skip_special_tokens=True) for k in range(GRPO_K)]
-        acts         = [parse_response(r) for r in resps]
-        reward_dicts = [score_response(a, sc, r, level=curr_level, fmt_ema=format_ema) for a, r in zip(acts, resps)]
-        rewards      = torch.tensor([rd["total"] for rd in reward_dicts], dtype=torch.float32, device="cuda")
         if rewards.std().item() < 1e-6:
             rewards = rewards + torch.randn_like(rewards) * 0.01
         adv = (rewards - rewards.mean()) / (rewards.std() + 1e-8)
         adv = adv.clamp(-2.0, 2.0)
-        format_ema = 0.1 * (sum(1 for a in acts if a.get("__valid__")) / GRPO_K) + 0.9 * format_ema
         FastLanguageModel.for_training(model)
         optimizer.zero_grad()
         for r_text, a_val in zip(resps, adv.tolist()):
-            f_enc = tokenizer(prompt + r_text, return_tensors="pt", truncation=True, max_length=1280).to("cuda")
-            lbls  = f_enc.input_ids.clone()
             lbls[:, :prompt_len] = -100
-            loss  = model(input_ids=f_enc.input_ids, attention_mask=f_enc.attention_mask, labels=lbls).loss
             (loss * a_val / GRPO_K).backward()
             del f_enc, lbls, loss
         torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
         optimizer.step()
         if step % 10 == 0:
-            comp = {k: sum(rd.get(k, 0) for rd in reward_dicts) / GRPO_K
-                    for k in ["decision","violation","citation","explanation","r_bonus","penalty"]}
             decs = Counter(a.get("decision", "INVALID") for a in acts)
             avg_r = rewards.mean().item()
             TRAIN_STATUS["reward"] = avg_r
@@ -350,13 +476,15 @@ for step in range(GRPO_STEPS):
             model.save_pretrained(ckpt_local)
             tokenizer.save_pretrained(ckpt_local)
             api.upload_folder(
-                folder_path     = ckpt_local,
-                repo_id         = CKPT_REPO,
-                path_in_repo    = f"step_{step}",
-                commit_message  = f"GRPO step {step} | reward={rewards.mean():.4f}",
-                token           = HF_TOKEN,
             )
-            import shutil; shutil.rmtree(ckpt_local, ignore_errors=True)
             print(f"  >> Pushed step_{step} to https://huggingface.co/{CKPT_REPO}")
             TRAIN_STATUS["phase"] = "GRPO"
@@ -376,11 +504,11 @@ print("\nSaving final model to HF Hub...")
 model.save_pretrained("/tmp/aegis_final")
 tokenizer.save_pretrained("/tmp/aegis_final")
 api.upload_folder(
-    folder_path    = "/tmp/aegis_final",
-    repo_id        = CKPT_REPO,
-    path_in_repo   = "final",
-    commit_message = "AEGIS final — 500 GRPO steps complete",
-    token          = HF_TOKEN,
 )
 print(f"Final model: https://huggingface.co/{CKPT_REPO}/tree/main/final")

 - Serves a minimal status page on :7860 so the Space stays alive
 - Prints "TRAINING COMPLETE - PLEASE DOWNGRADE HARDWARE" when done
 """
 import os, json, re, random, gc, sys, threading, time
 import torch
 import bitsandbytes as bnb
 from huggingface_hub import login, HfApi, hf_hub_download, snapshot_download
 from peft import set_peft_model_state_dict
+# CRITICAL: Import unsloth FIRST before any other ML libraries
+from unsloth import FastLanguageModel
 # ─── Auth & Config ────────────────────────────────────────────────────────────
+HF_TOKEN = os.environ["HF_TOKEN"]
 HF_USERNAME = os.environ.get("HF_USERNAME", "YashashMathur")
 STEP50_REPO = f"{HF_USERNAME}/aegis-step50"
+CKPT_REPO = f"{HF_USERNAME}/aegis-training-checkpoints"
 login(token=HF_TOKEN)
 api = HfApi()
 except Exception as e:
     print(f"Repo create: {e}")
+MAX_SEQ_LEN = 1536
+SFT_STEPS = 10  # 50 done, 10 remaining to reach 60
+GRPO_STEPS = 500
+GRPO_K = 4
+GRPO_LR = 5e-6
 CURRICULUM_SWITCH = 150
+GRAD_CLIP = 1.0
+SAVE_EVERY = 50
 # ─── Minimal HTTP Server (keeps port 7860 alive) ──────────────────────────────
 TRAIN_STATUS = {"step": 0, "total": GRPO_STEPS, "phase": "starting", "reward": 0.0}
 class StatusHandler(BaseHTTPRequestHandler):
     def do_GET(self):
         s = TRAIN_STATUS
         html = f"""<!DOCTYPE html><html><body style="font-family:monospace;padding:20px">
         <h2>AEGIS Training</h2>
+        <p>Phase: <b>{s["phase"]}</b></p>
+        <p>GRPO Step: <b>{s["step"]}/{s["total"]}</b></p>
+        <p>Avg Reward: <b>{s["reward"]:.4f}</b></p>
         <p>Checkpoint repo: <a href="https://huggingface.co/{CKPT_REPO}">{CKPT_REPO}</a></p>
         <meta http-equiv="refresh" content="30">
         </body></html>"""
         self.send_header("Content-type", "text/html")
         self.end_headers()
         self.wfile.write(html.encode())
+    def log_message(self, *args):
+        pass
 def start_server():
+    server = HTTPServer(("0.0.0.0", 7860), StatusHandler)
     server.serve_forever()
 threading.Thread(target=start_server, daemon=True).start()
 print("Status server running on :7860")
 with open("aegis_training_data_500.json") as f:
     raw = json.load(f)
 def sig(s):
+    parts = [
+        re.sub(r"\s+", " ", str(s.get(k, "")))
+        for k in ("worker_role", "worker_cot_trace", "worker_output")
+    ]
     return hashlib.md5("||".join(parts).encode()).hexdigest()
 dataset = list({sig(s): s for s in raw}.values())
 rng = random.Random(42)
 families = defaultdict(list)
     ("compound_violation", "overseer_manipulation"): 0.4,
 }
+W1 = {
+    "format": 0.40,
+    "decision": 0.25,
+    "violation": 0.15,
+    "citation": 0.10,
+    "explanation": 0.10,
+}
+W2 = {
+    "format": 0.10,
+    "decision": 0.35,
+    "violation": 0.25,
+    "citation": 0.20,
+    "explanation": 0.10,
+}
 def build_prompt(sc, tokenizer):
     cot = tokenizer.decode(
+        tokenizer.encode(sc.get("worker_cot_trace", ""), add_special_tokens=False)[
+            :800
+        ],
+        skip_special_tokens=True,
     )
     out = tokenizer.decode(
         tokenizer.encode(sc.get("worker_output", ""), add_special_tokens=False)[:400],
+        skip_special_tokens=True,
     )
     return (
         f"You are AEGIS, an AI fleet oversight agent. "
         f"Rules:\n{RULES_BLOCK}\nJSON:"
     )
 def parse_response(text):
     try:
         match = re.search(r"\{.*\}", text, re.DOTALL)
     except Exception:
         return {"__valid__": False}
 def score_response(a, truth, raw_text, level=1, fmt_ema=1.0):
     if not a.get("__valid__"):
+        return {
+            "total": -0.2,
+            "format": 0,
+            "decision": 0,
+            "violation": 0,
+            "citation": 0,
+            "explanation": 0,
+            "r_bonus": 0,
+            "penalty": 0,
+        }
     w = W1 if (level <= 1 and fmt_ema < 0.95) else W2
     d = 1.0 if a.get("decision") == truth.get("decision") else 0.0
     pred_v, exp_v = a.get("violation_type"), truth.get("violation_type")
     v = 1.0 if pred_v == exp_v else VIOLATION_SIM.get((pred_v, exp_v), 0.0)
     cited = str(a.get("policy_rule_cited") or "").strip().lower()
+    appl = truth.get("applicable_rules", [])
+    if appl and cited in [r.lower() for r in appl]:
+        c = 1.0
+    elif not appl and cited in ["", "none", "null"]:
+        c = 1.0
+    elif not appl and cited not in ["", "none", "null"]:
+        c = -0.1
+    else:
+        c = 0.0
     explanation = str(a.get("explanation", "")).lower()
     words = explanation.split()
     if len(words) < 5:
         e = 0.0
     elif exp_v != "none":
+        e = (0.5 if exp_v.replace("_", " ") in explanation else 0.0) + (
+            0.5 if any(r.lower() in explanation for r in appl) else 0.0
+        )
     else:
+        benign = [
+            "no violation",
+            "compliant",
+            "authorized",
+            "legitimate",
+            "allow",
+            "safe",
+            "benign",
+            "permitted",
+        ]
         e = 1.0 if any(m in explanation for m in benign) else 0.3
     if len(words) > 100:
         e *= max(0.4, 0.7 - (len(words) - 100) / 300)
     thought = re.search(r"<thought>(.*?)</thought>", raw_text, re.DOTALL)
     r_bonus = 0.20 if thought and len(thought.group(1).split()) >= 15 else 0.0
+    l_pen = -0.05 if len(raw_text) > 1400 else 0.0
     pred_d, exp_d = a.get("decision"), truth.get("decision")
+    penalty = 0.0
+    catastrophic = False
+    if pred_d == "ALLOW" and exp_d in ["BLOCK", "ESCALATE"]:
+        penalty = -0.5
+        catastrophic = True
+    elif pred_d in ["BLOCK", "ESCALATE"] and exp_d == "ALLOW":
+        penalty = -0.25
+    elif pred_d == "ESCALATE" and exp_d == "BLOCK":
+        penalty = -0.15
+    elif pred_d == "BLOCK" and exp_d == "ESCALATE":
+        penalty = -0.15
+    weighted = (
+        1.0 * w["format"]
+        + d * w["decision"]
+        + v * w["violation"]
+        + c * w["citation"]
+        + e * w["explanation"]
+        + r_bonus
+        + l_pen
+    )
+    total = (
+        min(1.0, weighted + penalty)
+        if catastrophic
+        else max(-0.3, min(1.0, weighted + penalty))
+    )
+    return {
+        "total": total,
+        "format": 1.0,
+        "decision": d,
+        "violation": v,
+        "citation": c,
+        "explanation": e,
+        "r_bonus": r_bonus,
+        "penalty": penalty,
+    }
 # ─── Load Model + Step-50 Checkpoint ─────────────────────────────────────────
 from unsloth import FastLanguageModel
     model,
     r=32,
     lora_alpha=16,
+    target_modules=[
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj",
+    ],
     lora_dropout=0,
     bias="none",
     use_gradient_checkpointing="unsloth",
 FastLanguageModel.for_training(model)
 if hasattr(model, "generation_config"):
     model.generation_config.max_length = None
+print(f"GPU: {torch.cuda.mem_get_info()[0] / 1e9:.1f} GB free\n")
 # ─── Remaining SFT (10 steps) ────────────────────────────────────────────────
 if SFT_STEPS > 0:
     sft_opt = torch.optim.AdamW(model.parameters(), lr=1e-4)
     model.train()
     for step in range(SFT_STEPS):
+        sc = random.choice(train_set)
+        prompt = build_prompt(sc, tokenizer)
+        vtype = sc.get("violation_type", "none")
         decision = sc["decision"]
+        rules = sc.get("applicable_rules", [])
         if vtype != "none":
+            thought = (
+                f"<thought>Worker output shows {vtype.replace('_', ' ')} patterns. "
+                f"Violates {', '.join(rules) if rules else 'policy'}. Decision: {decision}.</thought>"
+            )
         else:
+            thought = (
+                "<thought>Worker output appears compliant. No unauthorized access, "
+                "no policy violations detected. Safe to allow.</thought>"
+            )
+        target = thought + json.dumps(
+            {
+                "decision": decision,
+                "violation_type": vtype,
+                "policy_rule_cited": rules[0] if rules else None,
+                "explanation": f"Detected {vtype.replace('_', ' ')}"
+                if vtype != "none"
+                else "No violation detected",
+                "confidence": 0.9,
+            }
+        )
+        enc = tokenizer(
+            prompt + target,
+            return_tensors="pt",
+            truncation=True,
+            max_length=MAX_SEQ_LEN,
+        ).to("cuda")
         p_len = tokenizer(prompt, return_tensors="pt").input_ids.shape[1]
         labels = enc.input_ids.clone()
         labels[:, :p_len] = -100
         if (step + 1) % 4 == 0:
             sft_opt.step()
             sft_opt.zero_grad()
+        print(f"  SFT {step + 1}/{SFT_STEPS} | loss={loss.item():.4f}")
     del sft_opt
     torch.cuda.empty_cache()
     print("SFT complete.\n")
 # ─── GRPO Training ────────────────────────────────────────────────────────────
 TRAIN_STATUS["phase"] = "GRPO"
 FastLanguageModel.for_training(model)
+optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=GRPO_LR)
 format_ema = 0.0
 torch.cuda.empty_cache()
 gc.collect()
+print(f"GPU before GRPO: {torch.cuda.mem_get_info()[0] / 1e9:.1f} GB free")
 print(f"Starting GRPO: {GRPO_STEPS} steps / K={GRPO_K} / LR={GRPO_LR}\n")
 for step in range(GRPO_STEPS):
     TRAIN_STATUS["step"] = step
     torch.cuda.empty_cache()
     try:
+        sc = random.choice(train_set)
+        prompt = build_prompt(sc, tokenizer)
         curr_level = sc.get("level", 1) if step >= CURRICULUM_SWITCH else 1
+        p_enc = tokenizer(
+            prompt, return_tensors="pt", truncation=True, max_length=1024
+        ).to("cuda")
         prompt_len = p_enc.input_ids.shape[1]
+        temp = max(0.9, 1.3 - step * 0.0008)
         FastLanguageModel.for_inference(model)
         with torch.no_grad():
             gen = model.generate(
+                input_ids=p_enc.input_ids,
+                attention_mask=p_enc.attention_mask,
+                max_new_tokens=200,
+                temperature=temp,
+                top_p=0.9,
+                do_sample=True,
+                num_return_sequences=GRPO_K,
+                pad_token_id=tokenizer.eos_token_id,
             )
+        resps = [
+            tokenizer.decode(gen[k][prompt_len:], skip_special_tokens=True)
+            for k in range(GRPO_K)
+        ]
+        acts = [parse_response(r) for r in resps]
+        reward_dicts = [
+            score_response(a, sc, r, level=curr_level, fmt_ema=format_ema)
+            for a, r in zip(acts, resps)
+        ]
+        rewards = torch.tensor(
+            [rd["total"] for rd in reward_dicts], dtype=torch.float32, device="cuda"
+        )
         if rewards.std().item() < 1e-6:
             rewards = rewards + torch.randn_like(rewards) * 0.01
         adv = (rewards - rewards.mean()) / (rewards.std() + 1e-8)
         adv = adv.clamp(-2.0, 2.0)
+        format_ema = (
+            0.1 * (sum(1 for a in acts if a.get("__valid__")) / GRPO_K)
+            + 0.9 * format_ema
+        )
         FastLanguageModel.for_training(model)
         optimizer.zero_grad()
         for r_text, a_val in zip(resps, adv.tolist()):
+            f_enc = tokenizer(
+                prompt + r_text, return_tensors="pt", truncation=True, max_length=1280
+            ).to("cuda")
+            lbls = f_enc.input_ids.clone()
             lbls[:, :prompt_len] = -100
+            loss = model(
+                input_ids=f_enc.input_ids,
+                attention_mask=f_enc.attention_mask,
+                labels=lbls,
+            ).loss
             (loss * a_val / GRPO_K).backward()
             del f_enc, lbls, loss
         torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
         optimizer.step()
         if step % 10 == 0:
+            comp = {
+                k: sum(rd.get(k, 0) for rd in reward_dicts) / GRPO_K
+                for k in [
+                    "decision",
+                    "violation",
+                    "citation",
+                    "explanation",
+                    "r_bonus",
+                    "penalty",
+                ]
+            }
             decs = Counter(a.get("decision", "INVALID") for a in acts)
             avg_r = rewards.mean().item()
             TRAIN_STATUS["reward"] = avg_r
             model.save_pretrained(ckpt_local)
             tokenizer.save_pretrained(ckpt_local)
             api.upload_folder(
+                folder_path=ckpt_local,
+                repo_id=CKPT_REPO,
+                path_in_repo=f"step_{step}",
+                commit_message=f"GRPO step {step} | reward={rewards.mean():.4f}",
+                token=HF_TOKEN,
             )
+            import shutil
+            shutil.rmtree(ckpt_local, ignore_errors=True)
             print(f"  >> Pushed step_{step} to https://huggingface.co/{CKPT_REPO}")
             TRAIN_STATUS["phase"] = "GRPO"
 model.save_pretrained("/tmp/aegis_final")
 tokenizer.save_pretrained("/tmp/aegis_final")
 api.upload_folder(
+    folder_path="/tmp/aegis_final",
+    repo_id=CKPT_REPO,
+    path_in_repo="final",
+    commit_message="AEGIS final — 500 GRPO steps complete",
+    token=HF_TOKEN,
 )
 print(f"Final model: https://huggingface.co/{CKPT_REPO}/tree/main/final")