Task Generation for Agentic RL

• The paper introduces automated methods that generate structured, diverse, and executable tasks to train agentic reinforcement learning systems.
• It employs generative pipelines such as LLM-driven decomposition and curiosity-guided synthesis to calibrate task difficulty and ensure practical executability.
• Empirical outcomes demonstrate enhanced agent performance and scalable curriculum learning via synthetic task corpora and iterative verification strategies.

Task Generation for Agentic RL refers to the automated synthesis of structured, diverse, and executable tasks that support the systematic training and evaluation of agentic reinforcement learning (RL) agents—models capable of multi-step autonomous reasoning, tool use, and adaptive decision-making. In contrast to classical RL which revolves around a fixed, human-defined reward function, agentic RL settings require agents to learn how to interact with and reason about complex environments where suitable training tasks (with well-specified goals, rewards, and solution schemas) may be unavailable or prohibitively expensive to annotate. Task generation addresses this bottleneck by constructing synthetic or discovered task corpora, enabling scalable development, benchmarking, and improvement of agentic systems.

1. Formalizations and Problem Settings

Task Generation for agentic RL spans several problem settings and formalizations. In the classical curriculum RL paradigm, tasks are often defined as a distribution over goal states or logical specifications (e.g., via finite-trace Linear Temporal Logic formulas), with the objective to construct a progression (or curriculum) of tasks that lead the agent from simple behaviors to mastery of long-horizon, composite objectives (Florensa et al., 2017, Shukla et al., 2023). In open-ended settings, the environment is specified as a tuple $$\mathcal{E} = (\mathcal{S}, \mathcal{A}, \mathcal{P})$$—states, actions, transitions—with no pre-specified reward or goal set. The central challenge becomes defining a mapping $$F_{\text{task}}: \mathcal{E} \to \Delta(\mathcal{G})$$ from the environment to a distribution over tasks, such that generated tasks are diverse, executable, and relevant (Mai et al., 1 Dec 2025).

Key desiderata across settings include:

• Executability: generated tasks must be solvable by some agent in the current environment.
• Diversity: tasks should cover a breadth of entities, tools, and required skills.
• Difficulty Calibration: task complexity must match or escalate with the agent's proficiency, enabling curriculum learning.

2. Generative Methodologies and Pipelines

Recent frameworks instantiate a variety of generative pipelines:

• LLM-Driven Decomposition and Combinatorics: Pipelines such as TaskCraft generate atomic tasks by prompting LLMs over indexed documents and tool outputs, then extend them recursively via depth-based (“hop-extended”) and width-based (“multi-subtask”) transformations to synthesize structurally complex, hierarchically arranged challenges. Task verification involves both execution-based and judge-LLM filtering to ensure problem validity and answer concealment (Shi et al., 11 Jun 2025).
• Curiosity-Driven, Environment-Grounded Synthesis: CuES autonomously explores the environment guided by intrinsic novelty rewards, records trajectories, and abstracts action segments into reusable schemas. Candidate tasks are then vetted by re-execution and judged for consistency and executability, followed by curriculum-oriented rewrites to manipulate revealed information and create layered task sets (Mai et al., 1 Dec 2025).
• Adversarial Goal Generation: The Goal-GAN approach parameterizes goal/task distributions with generators trained adversarially to propose tasks of “intermediate difficulty”—i.e., in the regime where the agent's success rate is bounded away from both $0$ and $1$—thereby automatically shifting training focus to the edge of the agent's current competence (Florensa et al., 2017).
• Logic/Automaton-Guided Curriculum Synthesis: AGCL encodes high-level objectives in LTL$_f$, compiles them into deterministic finite automata, and constructs curriculum DAGs mapping automaton nodes to OOMDP-parameterized subtasks, ordered and weighted to optimize curriculum “smoothness” via a jump score metric (Shukla et al., 2023).
• Tool-Conditioned Task Synthesis: Frameworks such as ToolBrain automatically craft task queries requiring designated tools, controlling for task type distribution (minimum tool count, argument usage, complexity) and leveraging LLM self-ranking or filtering for quality. These tasks feed directly into RL fine-tuning loops (Le et al., 24 Sep 2025).
• Closed-Loop Co-Evolution with Self-Learning: Multi-role architectures such as ASL coordinate Prompt Generators, Policy Models, and Generative Reward Models in self-improving loops—task generation, execution, and scoring are all LLM-driven and co-adapt to the agent’s evolving proficiency, with explicit entropy-centric curriculum signals for continual escalation (Sun et al., 16 Oct 2025).

3. Control of Difficulty, Diversity, and Structure

Systematic control over task properties is a defining feature:

Framework	Task Difficulty Metric	Diversity Control	Structural Extensions
TaskCraft	$(d, w)$: depth (hops), width (subtasks); Difficulty score: $\alpha d + \beta w$ or $d \times w$	Merging orthogonal atomic tasks; empirical pass/fail calibration	Depth/width-based task extension
Goal-GAN	Empirical success rate bounds $[R_{\min}, R_{\max}]$ on goals	Adversarial generator explores new modes as agent coverage expands	N/A
CuES	Confidence threshold $\sigma_{i:j}$ on abstracted segments; rewrite-depth $L$	Trace deduplication, hint pool, energy-distance embedding	Sequential hint-based rewrites
AGCL	Average jump score $\overline{J}_\psi$ between curriculum steps	Curriculum DAG merges, transfer of knowledge to multiple paths	LTL$_f$-specified DAG curriculum
ASL	GRM-judged expected informativeness/entropy per task	Co-evolving task/solution/criteria, dynamic difficulty flag	Role-based iterative curriculum

In TaskCraft, for instance, users select $(d,w)$ regimes, use curriculum schedules (progressive escalation), and pilot with reference agents (e.g., Smolagents+) to empirically map difficulty to agent success rates (Shi et al., 11 Jun 2025). In ASL, tasks are generated in closed-loop manner, with the Prompt Generator explicitly incentivized to maximize uncertainty (entropy) in the Policy Model’s responses, thus automatically targeting the frontier of learning (Sun et al., 16 Oct 2025).

4. Verification, Validation, and Trajectory Generation

Automated pipelines require stringent quality control:

• LLM- and Agent-Based Verification: TaskCraft requires atomic tasks to be solvable by an agent but unsolvable by a bare LLM, filtering unsound or trivial queries. Extension tasks are LLM-verified to ensure superset relations, coherence, and no answer leakage (Shi et al., 11 Jun 2025).
• Execution and Memory-Based Judgement: CuES re-executes candidate traces and retains only goals that can be matched by a compliant agent trace, further supported by confidence scoring and deduplication (Mai et al., 1 Dec 2025).
• Co-evolving Generative Reward Models: In ASL, a Generative Reward Model is trained to judge task/answer pairs; continual co-evolution with the Prompt Generator and Policy Model avoids reward hacking and supports reliable escalation (Sun et al., 16 Oct 2025).

Generated tasks are paired with execution trajectories—instrumented records of tool invocations, stepwise agent reasoning, and answers—supporting their use as demonstrations for supervised fine-tuning or as guides for reward shaping (e.g., via intermediate rewards per correct tool call) (Shi et al., 11 Jun 2025).

5. Integration into RL Training Loops and Empirical Outcomes

Synthetically generated tasks form corpora for RL training:

• Dataset Assembly and Sampling: TaskCraft assembles a synthetic dataset of ≈36,000 tasks stratified by difficulty, with trajectories available for behavior cloning or policy optimization (Shi et al., 11 Jun 2025). CuES samples uniformly from its synthesized task set for episodic RL (Mai et al., 1 Dec 2025).
• Reward and Shaping Mechanisms: Reward structures include sparse rewards (final correctness), dense shaping (intermediate retrieval/step rewards), and KL-regularization toward demonstration policies. Trajectories provide examples for behavior cloning or seeds for curriculum RL (Shi et al., 11 Jun 2025).
• Curriculum Schedules: Data-driven curricula expose the agent to tasks of escalating complexity—controlled by $(d,w)$, tool count, or empirically estimated success rates—enabling staged acquisition of transferable skills or tool-use capabilities (Shi et al., 11 Jun 2025, Florensa et al., 2017).
• Empirical Outcomes: Use of synthetic task generation improves prompt optimization workflows, enriches supervised fine-tuning, and yields interpretable curricula and benchmarks—demonstrated by increased agent performance, transfer capacity, and task coverage relative to baseline RL and static instruction datasets. For example, CuES-trained agents achieve macro-averaged task success rates of ∼51.2% versus ∼36% for non-CuES backbones (Mai et al., 1 Dec 2025).

6. Comparison of Major Approaches

Approach	Core Generation Mechanism	Verification	Complexity Control	Notable Outcomes
TaskCraft	LLM-prompted atomic + compositional	Agent + JudgeLLM	$(d,w)$-based, user tunable	36k tasks, transferable skills
Goal-GAN	GAN-trained goal generator	Success rate	Intermediate passband	Fast coverage, curriculum
CuES	Intrinsic curiosity + abstraction	Re-execution	Confidence, rewrite-depth	Diversity, macro success gains
ToolBrain	Tool-spec conditioned LLM generation	Self-rank/score	min tool calls, word count	Rapid tool-use acquisition
AGCL	DFA (LTL$_f$)+ OOMDP curriculum DAG	Acceptance trace	Smoothness (jump score)	3–5× sample efficiency boost
ASL	RL-fine-tuned Prompt Generator (PG)	Co-evolving GRM	Entropy, GRM threshold	Continual, self-improving loop

7. Limitations, Insights, and Future Directions

Key limitations include computational/resource intensiveness due to large-scale exploratory rollouts (CuES), dependence on accurate task and reward modeling (ASL, AGCL), and scalability constraints around environment or logic complexity (AGCL’s DFA-to-curriculum mapping). Diverse verification strategies (agent, LLM, VLM) address but do not wholly eliminate issues of false positives or unsolvable tasks.

Future research is directed toward on-policy task generation, theoretically grounded task complexity metrics, with expansion to multi-agent, more open-ended or less-structured environments, and partial automation of high-level task specification extraction (e.g., LTL induction from demonstrations) (Mai et al., 1 Dec 2025, Shukla et al., 2023). Application to tool-use, autonomous QA, program synthesis, and robotic skill libraries demonstrates the critical role principled task generation now plays in the maturation of agentic RL.

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References:

• TaskCraft: Automated Generation of Agentic Tasks (Shi et al., 11 Jun 2025)
• CuES: A Curiosity-driven and Environment-grounded Synthesis Framework for Agentic RL (Mai et al., 1 Dec 2025)
• Automatic Goal Generation for Reinforcement Learning Agents (Florensa et al., 2017)
• Automaton-Guided Curriculum Generation for Reinforcement Learning Agents (Shukla et al., 2023)
• ToolBrain: A Flexible Reinforcement Learning Framework for Agentic Tools (Le et al., 24 Sep 2025)
• Towards Agentic Self-Learning LLMs in Search Environment (Sun et al., 16 Oct 2025)