Fix multi-turn generator token budget accounting

examples/train/mini_swe_agent/README.md

@@ -58,7 +58,7 @@ For issues with SkyRL or the Mini-SWE-Agent integration, please [open an Issue](
 
 ### Common Issues
 
-- **Context length errors**: If you see `ValueError: The decoder prompt (length xxxx) is longer than the maximum model length`, increase `max_input_length` and `max_generate_length` or reduce steps in `swebench.yaml`.
+- **Context length errors**: If you see `ValueError: The decoder prompt (length xxxx) is longer than the maximum model length`, increase the vLLM `engine_init_kwargs.max_model_len`, reduce `max_input_length`, or reduce steps in `swebench.yaml`. `max_generate_length` is the assistant-token budget for a trajectory and does not increase the model context window.
 
 - **All zero rewards**: If rewards are consistently zero, the task may be too difficult. Consider:
   - Filtering data for a better mix of easy/hard samples

examples/train/mini_swe_agent/mini_swe_generator.py

@@ -1,28 +1,37 @@
 import asyncio
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Any, Tuple
-import yaml
 import traceback
-import ray
+from dataclasses import dataclass
 from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
 
-from minisweagent.models import get_model
+import ray
+import yaml
 from minisweagent.agents.default import DefaultAgent
-from minisweagent.run.utils.save import save_traj
 from minisweagent.config import get_config_path
-from .mini_swe_utils import evaluate_trajectory, get_sb_environment
+from minisweagent.models import get_model
+from minisweagent.run.utils.save import save_traj
 
-from skyrl.train.config import GeneratorConfig, SkyRLGymConfig
-from skyrl.train.generators.skyrl_gym_generator import SkyRLGymGenerator, GeneratorOutput, GeneratorInput
-from skyrl.train.generators.base import TrajectoryID, TrainingPhase, BatchMetadata
 from skyrl.backends.skyrl_train.inference_engines.base import ConversationType
-from skyrl.backends.skyrl_train.inference_engines.inference_engine_client import InferenceEngineClient
-from skyrl.backends.skyrl_train.inference_engines.utils import get_sampling_params_for_backend
+from skyrl.backends.skyrl_train.inference_engines.inference_engine_client import (
+    InferenceEngineClient,
+)
+from skyrl.backends.skyrl_train.inference_engines.utils import (
+    get_sampling_params_for_backend,
+)
+from skyrl.train.config import GeneratorConfig, SkyRLGymConfig
+from skyrl.train.generators.base import BatchMetadata, TrainingPhase, TrajectoryID
+from skyrl.train.generators.skyrl_gym_generator import (
+    GeneratorInput,
+    GeneratorOutput,
+    SkyRLGymGenerator,
+)
 from skyrl.train.generators.utils import (
-    get_rollout_metrics,
     get_response_ids_and_loss_mask_from_messages,
+    get_rollout_metrics,
 )
 
+from .mini_swe_utils import evaluate_trajectory, get_sb_environment
+
 
 @dataclass
 class MiniSWEGeneratorConfig(GeneratorConfig):
@@ -199,15 +208,27 @@ async def minisweagent_agent_loop(
         # Extract prompt ids
         prompt_ids = initial_input_ids
 
-        # Calculate maximum response tokens allowed
-        max_response_tokens = max_tokens + max_input_length - initial_prompt_length
+        # Truncate by assistant-token budget first. Environment/user observations are kept only
+        # insofar as they fit the secondary packed-sequence guard below; they do not consume
+        # max_generate_length because their loss mask is 0.
+        assistant_tokens = 0
+        assistant_budget_response_tokens = len(response_ids)
+        assistant_budget_exceeded = False
+        for idx, mask in enumerate(loss_mask):
+            assistant_tokens += int(bool(mask))
+            if assistant_tokens > max_tokens:
+                assistant_budget_response_tokens = idx
+                assistant_budget_exceeded = True
+                break
+
+        # Keep the packed prompt+response sequence bounded for training tensor sizes.
+        packed_response_tokens = max(0, max_tokens + max_input_length - initial_prompt_length)
+        max_response_tokens = min(assistant_budget_response_tokens, packed_response_tokens)
 
-        # Determine stop reason
         stop_reason = "complete"  # Default for trial completion
-        if len(response_ids) > max_response_tokens:
+        if assistant_budget_exceeded or len(response_ids) > packed_response_tokens:
             stop_reason = "length"
 
-        # Truncate to maximum allowed length
         response_ids = response_ids[:max_response_tokens]
         loss_mask = loss_mask[:max_response_tokens]
 

skyrl/train/config/config.py

@@ -424,6 +424,9 @@ class FullyAsyncConfig(BaseConfig):
 @dataclass
 class SamplingParams(BaseConfig):
     max_generate_length: int = 1024
+    """Trajectory-level assistant/generated-token budget. In multi-turn generators,
+    environment observation tokens are loss-masked and do not count against this budget.
+    The vLLM request field is ``max_tokens`` and may be reduced per turn to fit context."""
     repetition_penalty: float = 1.0
     temperature: float = 1.0
     top_p: float = 1.0
@@ -496,7 +499,9 @@ class InferenceEngineConfig(BaseConfig):
     """When True, pass ``language_model_only=True`` to the vLLM engine so that
     multimodal models (e.g. Qwen3.5) skip vision encoder initialization."""
     engine_init_kwargs: Dict[str, Any] = field(default_factory=dict)
-    """Pass-through kwargs for the vLLM engine. Names must match the engine's args."""
+    """Pass-through kwargs for the vLLM engine. Names must match the engine's args. If
+    ``max_model_len`` is set, rollout requests are capped so input tokens plus per-request
+    generated tokens fit within that window."""
     override_existing_update_group: str = "auto"
     """``"auto"``, ``"enable"``, or ``"disable"``."""
     external_proxy_url: Optional[str] = None
@@ -528,7 +533,8 @@ class GeneratorConfig(BaseConfig):
     batched: bool = False
     max_turns: int = 1
     max_input_length: Optional[int] = None
-    """Max generator input length for multi-turn conversations. For single-turn, set equal to ``max_prompt_length``."""
+    """Max input/context length allowed before each generation turn. For single-turn, set
+    equal to ``max_prompt_length``. Distinct from ``sampling_params.max_generate_length``."""
     chat_template: ChatTemplateConfig = field(default_factory=ChatTemplateConfig)
     chat_template_kwargs: Dict[str, Any] = field(default_factory=dict)
     """Kwargs passed to ``tokenizer.apply_chat_template``."""

skyrl/train/config/ppo_base_config.yaml

@@ -295,7 +295,9 @@ generator:
   n_samples_per_prompt: 5
   async_engine: true
   batched: false
-  max_input_length: ${trainer.max_prompt_length} # max generator input length used for multi-turn conversations - for single turn set equal to max_prompt_length
+  # Max input/context length checked before each generation turn. For single-turn, set equal to max_prompt_length.
+  # This is distinct from sampling_params.max_generate_length, which budgets assistant-generated tokens.
+  max_input_length: ${trainer.max_prompt_length}
   # VLLM_ENABLE_V1_MULTIPROCESSING=0 for reproducibility
   vllm_v1_disable_multiproc: true
   enable_prefix_caching: true
@@ -334,11 +336,14 @@ generator:
 
   # Inference engine arguments. Arguments are passed directly to the vLLM engine, so names must match
   # the engine's args. To specify an engine arg in the CLI override, use the format: +generator.engine_init_kwargs.arg_name=value
+  # If max_model_len is set, each rollout request's max_tokens is capped so prompt+completion fits this window.
   engine_init_kwargs: {}
 
   override_existing_update_group: "auto" # "auto", "enable", "disable"
   # sampling params for generation phase
   sampling_params:
+    # Trajectory-level assistant/generated-token budget. Multi-turn environment observations are loss-masked
+    # and do not count against this value.
     max_generate_length: 1024
     repetition_penalty: 1.0
     temperature: 1.0
@@ -395,4 +400,4 @@ generator:
 environment:
   env_class: "gsm8k"
   # NOTE: environment specific defaults for environment.skyrl_gym are set at the following path:
-  # skyrl_gym: config/skyrl_gym_config/default.yaml
+  # skyrl_gym: config/skyrl_gym_config/default.yaml

skyrl/train/generators/skyrl_gym_generator.py

@@ -8,7 +8,7 @@
 import asyncio
 import copy
 from concurrent.futures import ThreadPoolExecutor
-from dataclasses import asdict, dataclass
+from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Tuple, Union
 from uuid import uuid4
 
@@ -33,9 +33,13 @@
 )
 from skyrl.train.generators.utils import (
     apply_overlong_filtering,
+    compute_request_max_tokens,
     get_custom_chat_template,
     get_generation_prompt_ids,
+    get_max_model_len,
     get_rollout_metrics,
+    normalize_sampling_params,
+    sampling_params_with_max_tokens,
 )
 from skyrl_gym.envs.base_text_env import BaseTextEnvStepOutput
 
@@ -330,11 +334,12 @@ async def agent_loop(
         loss_mask = []  # this excludes the prompt
         rollout_logprobs = None
 
-        # `sampling_params` if provided is a dict in the format expected by the inference engine backend
-        # we cast default config to a dict for consistency
-        current_sampling_params: dict = (
-            sampling_params if sampling_params is not None else asdict(self.generator_cfg.sampling_params)
-        )
+        # `sampling_params` if provided is a dict in the format expected by the inference engine backend.
+        # When absent, normalize the config dataclass into the backend shape here so agent_loop() direct
+        # callers receive the same behavior as the main generator path.
+        base_sampling_params = normalize_sampling_params(self.generator_cfg, sampling_params)
+        max_model_len = get_max_model_len(self.generator_cfg)
+        generated_tokens_used = 0
 
         # Accumulate per-step rewards. Format: (reward, response_end_token_idx)
         per_step_rewards: List[Tuple[float, Optional[int]]] = []
@@ -343,7 +348,7 @@ async def agent_loop(
 
         agent_loop_output = StepWiseOutput(step_outputs=[]) if is_step_wise else None
 
-        get_logprobs = self.generator_cfg.sampling_params.logprobs is not None
+        get_logprobs = base_sampling_params.get("logprobs", None) is not None
         agent_loop_state = AgentLoopState(
             chat_history=chat_history,
             input_ids=initial_input_ids,
@@ -352,6 +357,7 @@ async def agent_loop(
             response_end_idx=None,
             done=False,
         )
+        new_obs: ConversationType = []
 
         while not agent_loop_state.done:
 
@@ -373,8 +379,20 @@ async def agent_loop(
                 agent_loop_state.loss_mask = []
                 agent_loop_state.rollout_logprobs = None
 
+            request_max_tokens = compute_request_max_tokens(
+                max_tokens - generated_tokens_used,
+                len(agent_loop_state.input_ids),
+                max_model_len,
+            )
+            if request_max_tokens <= 0:
+                stop_reason = "length"
+                break
+
+            current_sampling_params = sampling_params_with_max_tokens(base_sampling_params, request_max_tokens)
             engine_input = InferenceEngineInput(
-                prompt_token_ids=[agent_loop_state.input_ids], session_ids=[session_id], sampling_params=sampling_params
+                prompt_token_ids=[agent_loop_state.input_ids],
+                session_ids=[session_id],
+                sampling_params=current_sampling_params,
             )
             engine_output = await self.inference_engine_client.generate(engine_input, model=self.policy_model_name)
             output = engine_output["responses"][0]
@@ -440,6 +458,22 @@ async def agent_loop(
             if turn_output.rollout_expert_indices is not None and agent_loop_state.rollout_expert_indices is None:
                 agent_loop_state.rollout_expert_indices = []
 
+            turn_generated_tokens = sum(turn_output.get_turn_loss_mask())
+            if not self.use_conversation_multi_turn and not retokenize_chat_history:
+                new_resp_tokens = turn_output.output_ids.copy()
+                if new_resp_tokens and new_resp_tokens[-1] == self.tokenizer.eos_token_id:
+                    new_resp_tokens = new_resp_tokens[:-1]
+                turn_generated_tokens = len(new_resp_tokens)
+            generated_tokens_used += turn_generated_tokens
+            if generated_tokens_used >= max_tokens and not agent_loop_state.done:
+                # The trajectory-level assistant budget is exhausted. Do not append the
+                # next observation, since there will be no following generation request.
+                stop_reason = "length"
+                agent_loop_state.done = True
+                new_obs = []
+                turn_output.new_obs = []
+                turn_output.obs_ids = []
+
             if is_step_wise:
                 # current response + observation ids
                 turn_response_ids = turn_output.output_ids + turn_output.obs_ids
@@ -489,6 +523,23 @@ async def agent_loop(
         rollout_expert_indices_out = None
         response_ids = None
 
+        if not is_step_wise and not retokenize_chat_history and agent_loop_state.response_end_idx is None:
+            agent_loop_output = TrajectoryOutput(
+                response_ids=[],
+                reward=[],
+                stop_reason=stop_reason,
+                loss_mask=[],
+                prompt_ids=prompt_ids,
+                rollout_logprobs=[] if get_logprobs else None,
+                env_metrics=env_metrics,
+                rollout_expert_indices=None,
+            )
+            return self._post_process_agent_loop_output(
+                agent_loop_output,
+                env_extras,
+                trajectory_id,
+            )
+
         # Prepare the final loss_mask, response_ids and rollout_logprobs .
         # We remove the final observation messages /token IDs here
         # Note that during the agent loop, we still add the final observation messages/ tokens because we terminate the agent loop if the input length
@@ -531,17 +582,21 @@ async def agent_loop(
         if not self.use_conversation_multi_turn:
             assert response_ids is not None and loss_mask is not None
             if stop_reason != "length" and response_ids and response_ids[-1] != self.tokenizer.eos_token_id:
-                response_ids.append(self.tokenizer.eos_token_id)
-                # TODO(Charlie): this should be 0? Otherwise logprobs will be extremely off. But if it is loss
-                # masked with 0, why bother adding it?
-                loss_mask.append(1)
-                if rollout_logprobs is not None:
-                    rollout_logprobs.append(0.0)
-                if rollout_expert_indices_out is not None and rollout_expert_indices_out:
-                    layer_num = len(rollout_expert_indices_out[0])
-                    topk = len(rollout_expert_indices_out[0][0]) if layer_num > 0 else 0
-                    rollout_expert_indices_out.append([[0] * topk for _ in range(layer_num)])
-                appended_eos_token = True
+                if generated_tokens_used < max_tokens:
+                    response_ids.append(self.tokenizer.eos_token_id)
+                    # TODO(Charlie): this should be 0? Otherwise logprobs will be extremely off. But if it is loss
+                    # masked with 0, why bother adding it?
+                    loss_mask.append(1)
+                    if rollout_logprobs is not None:
+                        rollout_logprobs.append(0.0)
+                    if rollout_expert_indices_out is not None and rollout_expert_indices_out:
+                        layer_num = len(rollout_expert_indices_out[0])
+                        topk = len(rollout_expert_indices_out[0][0]) if layer_num > 0 else 0
+                        rollout_expert_indices_out.append([[0] * topk for _ in range(layer_num)])
+                    generated_tokens_used += 1
+                    appended_eos_token = True
+                else:
+                    stop_reason = "length"
 
         if self.generator_cfg.step_wise_trajectories:
             for per_step_output, (reward, resp_end_idx) in zip(agent_loop_output.step_outputs, per_step_rewards):
@@ -713,7 +768,10 @@ async def generate_batched(
             tokenize=True,
             return_dict=False,
         )
-        engine_input = InferenceEngineInput(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
+        request_sampling_params = sampling_params_with_max_tokens(
+            normalize_sampling_params(self.generator_cfg, sampling_params), max_tokens
+        )
+        engine_input = InferenceEngineInput(prompt_token_ids=prompt_token_ids, sampling_params=request_sampling_params)
         engine_output = await self.inference_engine_client.generate(engine_input, model=self.policy_model_name)
         outputs = engine_output["responses"]
         responses = engine_output["response_ids"]
@@ -788,7 +846,8 @@ async def generate(self, input_batch: GeneratorInput, disable_tqdm: bool = False
         if self.generator_cfg.step_wise_trajectories:
             assert trajectory_ids is not None, "`trajectory_ids` is a required field for step wise training"
         sampling_params: Optional[dict] = input_batch.get("sampling_params", None)
-        max_tokens = self.generator_cfg.sampling_params.max_generate_length
+        base_sampling_params = normalize_sampling_params(self.generator_cfg, sampling_params)
+        max_tokens = int(base_sampling_params.get("max_tokens", self.generator_cfg.sampling_params.max_generate_length))
         max_input_length = self.generator_cfg.max_input_length
 
         if self.batched: