Agentic Reinforcement Learning (Agentic RL) trains LLM agents as autonomous decision-makers in dynamic environments, extending standard LLM RL from single-turn text generation to multi-turn, partially observable Markov decision processes (POMDPs) with long-horizon planning, tool use, and adaptive behavior.

Agentic RL faces two fundamental challenges that standard LLM RL does not:

1. Sparse, non-instructive rewards: Binary 0/1 rewards at the end of long trajectories provide minimal learning signal. The agent must discover which of its many actions contributed to success or failure.

2. Credit assignment over long horizons: With trajectories spanning dozens of tool calls, attributing reward to specific actions is extremely difficult. Standard policy gradient estimators have high variance in this setting.

Progressive reward shaping (arXiv:2512.07478) addresses sparse rewards by building agent capabilities incrementally:

• Start with dense, easy-to-earn intermediate rewards
• Gradually shift toward sparse outcome rewards as the agent improves
• Curriculum over reward complexity mirrors curriculum over task complexity

# Progressive reward shaping concept
def progressive_reward(trajectory, stage):
    """Reward function that evolves with training stage."""
    outcome_reward = verify_final_answer(trajectory)
 
    if stage == "early":
        # Dense rewards: reward correct tool selection, format, partial progress
        step_rewards = [score_step(s) for s in trajectory.steps]
        return 0.7 * mean(step_rewards) + 0.3 * outcome_reward
 
    elif stage == "middle":
        # Mixed: some step rewards, heavier outcome weight
        key_steps = [score_step(s) for s in trajectory.key_milestones]
        return 0.3 * mean(key_steps) + 0.7 * outcome_reward
 
    else:  # "late"
        # Sparse: pure outcome reward (RLVR-style)
        return outcome_reward

The paper introduces Value-based Sampling Policy Optimization, which uses a learned value function to select high-quality training trajectories, improving sample efficiency.

Verl-Tool (submitted to ICLR 2026) provides an open-source framework for holistic agentic RL with tool use:

• Tool-integrated training: Supports RL optimization over full tool-interaction trajectories
• Multiple tool types: Code execution, web search, calculator, API calls
• Scalable infrastructure: Distributed training across multiple GPUs
• Flexible reward: Supports both verifiable rewards (RLVR) and learned reward models

Verl-Tool bridges the gap between RLVR (which works for single-turn verifiable tasks) and full agentic environments requiring multi-turn tool use.

RLVR (Reinforcement Learning from Verifiable Rewards) uses deterministic reward functions, but is limited to tasks with clear correct answers. Agentic RL extends this to:

• Open-ended tasks without single correct answers
• Multi-step tool interaction trajectories
• Environments with stochastic dynamics
• Tasks requiring exploration and discovery

The progression: RLHF → RLVR → Agentic RL represents increasing automation of the reward signal and increasing complexity of the training environment.

RL trains LLM agents to autonomously select, sequence, and adapt tool use through environment feedback:

• Tool selection: Learn when to call which tool based on current state
• Argument generation: Learn to construct correct tool arguments from context
• Error recovery: Learn to handle tool failures and retry with different strategies
• Composition: Learn to chain multiple tools to solve complex tasks

Unlike supervised fine-tuning on tool-use demonstrations, RL enables agents to discover novel tool-use strategies through trial and error.

A typical agentic RL training loop:

1. Environment step: Agent observes state (conversation history, tool results, task description)
2. Policy step: LLM generates next action (tool call or text response)
3. Execution step: Tool is executed in sandboxed environment, result appended to context
4. Reward step: At trajectory end, compute reward (verifiable check, or shaped intermediate reward)
5. Update step: Update policy via PPO, GRPO, or REINFORCE with trajectory-level advantages

• Coding agents: Write, test, debug code across multiple files using execution feedback
• Research agents: Search, read, synthesize information using web tools
• Data analysis agents: Query databases, run computations, generate visualizations
• Scientific agents: Design experiments, run simulations, analyze results
• Multi-agent collaboration: Teams of specialized agents coordinating on complex tasks

• Era of experience: Agents learning through self-generated experience rather than human demonstrations (Silver & Sutton, 2025)
• Group reasoning: Multi-agent RL where agents reason collaboratively
• Slow reasoning augmentation: Combining structured CoT with RL for deeper planning
• Scientific agents: NVIDIA's work on training scientific agents with RL for hypothesis generation and experimental design
• Safety and alignment: Ensuring agentic RL produces agents that respect boundaries and avoid harmful actions

• arXiv:2512.07478 - Enhancing Agentic RL with Progressive Reward Shaping
• Verl-Tool: Holistic Agentic RL with Tool Use (ICLR 2026)
• arXiv:2509.02547 - Agentic RL Taxonomy and Survey

• Agent RLVR - RL from Verifiable Rewards for agents
• Process Reward Models - Step-level rewards for training signal
• Speculative Tool Execution - Optimizing agent tool call latency
• Parallel Function Calling - Concurrent tool execution