Reasoning and Tool-use Compete in Agentic RL:From Quantifying Interference to Disentangled Tuning
Researchers demonstrate that jointly training language models for both reasoning and tool-use in agentic RL creates measurable performance interference. They introduce DART, a framework that decouples these capabilities through separate low-rank adaptation modules, achieving superior results across thirteen benchmarks and approaching theoretical performance limits.
This research addresses a fundamental assumption in agentic reinforcement learning that has remained largely unexamined: whether training a single model for both reasoning and tool execution actually improves overall performance. The authors use Capability Effect Attribution to quantify interference between these two distinct behavioral modes, finding that misaligned gradient directions during joint optimization create training conflicts that degrade agent effectiveness.
The work builds on growing recognition within the machine learning community that multi-task learning can create negative transfer effects when task objectives compete. In agentic systems specifically, the shift toward interleaving reasoning steps with external tool calls has been considered universally beneficial without rigorous empirical validation. This research provides concrete evidence that the paradigm requires refinement.
Developing AI systems that reliably perform complex reasoning and tool-use creates significant value across enterprise applications, from retrieval-augmented generation systems to SQL generation workflows. By demonstrating that decoupled parameter updates substantially improve performance while remaining computationally efficient, DART offers a practical architectural improvement that could accelerate adoption of agentic systems in production environments.
The implications extend beyond immediate performance gains. If separate optimization of reasoning and tool-use becomes standard practice, it suggests future agentic systems may benefit from modular architectures that isolate different behavioral capabilities. This finding could influence how researchers and practitioners design large language model systems for complex task solving.
- →Joint training of reasoning and tool-use in agentic RL creates measurable interference through misaligned gradient directions.
- →DART's decoupled approach using separate low-rank adaptation modules outperforms joint-optimization baselines across thirteen benchmarks.
- →The research challenges the prevailing assumption that single-parameter optimization benefits overall agentic performance.
- →Modular architectures for AI agents may become standard practice as evidence mounts against shared parameter training.
- →Performance improvements from capability decoupling suggest efficiency gains without additional computational overhead.