diff --git a/example_trainer/vllm_api_server.py b/example_trainer/vllm_api_server.py
index 2923098a..79325ea7 100644
--- a/example_trainer/vllm_api_server.py
+++ b/example_trainer/vllm_api_server.py
@@ -287,6 +287,206 @@ async def _generate(request_dict: dict, raw_request: Request) -> Response:
     return JSONResponse(ret)
 
 
+# =============================================================================
+# OpenAI-Compatible Chat Completions Endpoint
+# =============================================================================
+
+
+@app.post("/v1/chat/completions")
+async def openai_chat_completions(request: Request) -> Response:
+    """
+    OpenAI-compatible chat completions endpoint.
+    
+    This is a thin wrapper around our /generate endpoint that formats
+    the request/response to match OpenAI's chat completions API.
+    
+    Used by atroposlib/GSM8k environment for rollout generation.
+    """
+    if engine is None:
+        raise HTTPException(status_code=503, detail="Engine not initialized")
+    
+    import time as time_module
+    request_dict = await request.json()
+    
+    # Extract parameters
+    model = request_dict.get("model", "")
+    messages = request_dict.get("messages", [])
+    max_tokens = request_dict.get("max_tokens", 256)
+    temperature = request_dict.get("temperature", 1.0)
+    top_p = request_dict.get("top_p", 1.0)
+    n = request_dict.get("n", 1)
+    stop = request_dict.get("stop", None)
+    presence_penalty = request_dict.get("presence_penalty", 0.0)
+    frequency_penalty = request_dict.get("frequency_penalty", 0.0)
+    
+    # Convert messages to prompt using tokenizer's chat template
+    try:
+        prompt = engine.tokenizer.apply_chat_template(
+            messages, 
+            tokenize=False, 
+            add_generation_prompt=True
+        )
+    except Exception:
+        # Fallback: simple concatenation if no chat template
+        prompt = ""
+        for msg in messages:
+            role = msg.get("role", "user")
+            content = msg.get("content", "")
+            prompt += f"{role}: {content}\n"
+        prompt += "assistant: "
+    
+    # Build sampling params (reusing our existing logic)
+    sampling_params = SamplingParams(
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        n=n,
+        stop=stop,
+        presence_penalty=presence_penalty,
+        frequency_penalty=frequency_penalty,
+    )
+    
+    request_id = random_uuid()
+    
+    final_output = None
+    async for request_output in engine.generate(prompt, sampling_params, request_id):
+        final_output = request_output
+    
+    if final_output is None:
+        raise HTTPException(status_code=500, detail="Generation failed")
+    
+    # Build choices in OpenAI chat format
+    choices = []
+    for idx, output in enumerate(final_output.outputs):
+        choices.append({
+            "index": idx,
+            "message": {
+                "role": "assistant",
+                "content": output.text,
+            },
+            "finish_reason": output.finish_reason or "stop",
+        })
+    
+    # Build response
+    prompt_tokens = len(final_output.prompt_token_ids)
+    completion_tokens = sum(len(o.token_ids) for o in final_output.outputs)
+    
+    response = {
+        "id": f"chatcmpl-{random_uuid()}",
+        "object": "chat.completion",
+        "created": int(time_module.time()),
+        "model": model,
+        "choices": choices,
+        "usage": {
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "total_tokens": prompt_tokens + completion_tokens,
+        },
+    }
+    
+    return JSONResponse(response)
+
+
+@app.post("/v1/completions")
+async def openai_completions(request: Request) -> Response:
+    """
+    OpenAI-compatible text completions endpoint.
+    
+    This is the non-chat version of completions (raw text in, text out).
+    """
+    if engine is None:
+        raise HTTPException(status_code=503, detail="Engine not initialized")
+    
+    import time as time_module
+    request_dict = await request.json()
+    
+    # Extract parameters
+    model = request_dict.get("model", "")
+    prompt = request_dict.get("prompt", "")
+    max_tokens = request_dict.get("max_tokens", 256)
+    temperature = request_dict.get("temperature", 1.0)
+    top_p = request_dict.get("top_p", 1.0)
+    n = request_dict.get("n", 1)
+    stop = request_dict.get("stop", None)
+    presence_penalty = request_dict.get("presence_penalty", 0.0)
+    frequency_penalty = request_dict.get("frequency_penalty", 0.0)
+    logprobs_requested = request_dict.get("logprobs", None)
+    
+    # Handle single prompt or list of prompts
+    prompts = [prompt] if isinstance(prompt, str) else prompt
+    
+    # Build sampling params
+    sampling_params = SamplingParams(
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        n=n,
+        stop=stop,
+        presence_penalty=presence_penalty,
+        frequency_penalty=frequency_penalty,
+        logprobs=logprobs_requested,
+    )
+    
+    # Generate for each prompt
+    all_choices = []
+    total_prompt_tokens = 0
+    total_completion_tokens = 0
+    
+    for prompt_text in prompts:
+        request_id = random_uuid()
+        
+        final_output = None
+        async for request_output in engine.generate(prompt_text, sampling_params, request_id):
+            final_output = request_output
+        
+        if final_output is None:
+            raise HTTPException(status_code=500, detail="Generation failed")
+        
+        # Count tokens
+        total_prompt_tokens += len(final_output.prompt_token_ids)
+        
+        # Build choices
+        for output in final_output.outputs:
+            total_completion_tokens += len(output.token_ids)
+            
+            choice = {
+                "text": output.text,
+                "index": len(all_choices),
+                "logprobs": None,
+                "finish_reason": output.finish_reason or "stop",
+            }
+            
+            # Add logprobs if requested
+            if logprobs_requested is not None and output.logprobs:
+                choice["logprobs"] = {
+                    "tokens": [engine.tokenizer.decode([tid]) for tid in output.token_ids],
+                    "token_logprobs": [
+                        list(lp.values())[0].logprob if lp else None 
+                        for lp in output.logprobs
+                    ],
+                    "top_logprobs": None,
+                    "text_offset": [],
+                }
+            
+            all_choices.append(choice)
+    
+    # Build response in OpenAI format
+    response = {
+        "id": f"cmpl-{random_uuid()}",
+        "object": "text_completion",
+        "created": int(time_module.time()),
+        "model": model,
+        "choices": all_choices,
+        "usage": {
+            "prompt_tokens": total_prompt_tokens,
+            "completion_tokens": total_completion_tokens,
+            "total_tokens": total_prompt_tokens + total_completion_tokens,
+        },
+    }
+    
+    return JSONResponse(response)
+
+
 # =============================================================================
 # Bridge Endpoints (Weight Synchronization)
 # =============================================================================