2 links tagged with all of: reinforcement learning + agents + reasoning
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JOLT is a training method where one model plays both teacher and student, with the teacher trained to give feedback the student can actually learn from. The authors report gains in accuracy and token efficiency over GRPO on math, coding, and agentic tasks.
- Outcome-reward RL gives sparse feedback, and on long agentic tasks with rare successes that makes learning slow and sample-hungry.
- Privileged-information teachers (like a model that sees the verified solution) can be good at the task but poor at teaching it, since their guidance may exceed what the student can absorb.
- JOLT trains the teacher to be "reward-consistent," meaning its guidance pushes the student in the same direction as RL on the student's own reward. Reported gains over GRPO include +4.2pp on MATH, +3.0pp on LCBv6, and +9.9pp on AppWorld.
This paper introduces Qwen-AgentWorld, large language models trained to simulate agentic environments across seven domains using over 10 million interaction trajectories. The authors detail a three-stage training pipeline (CPT, SFT, RL) and evaluate on AgentWorldBench, showing superior simulation fidelity. They also demonstrate its use as both a standalone simulator for RL and a warm-up step that boosts downstream agent performance.
- Qwen-AgentWorld's 397B model held correct object-grasp sequences over 25 reasoning steps in a robotics task without losing context, beating existing simulators on state accuracy and reasoning-chain length.
- RL agents trained inside Qwen-AgentWorld's simulated environments outperformed agents trained only in the real world by 15–20%.
- Using the world model's weights to warm-start a unified agent boosted performance across seven benchmarks and sometimes cut required training runs in half.