Originally published on AI Tech Connect.
What you need to know The paradigm has shifted — agentic RL reframes the model as a proactive agent. Training optimises the trajectory (reasoning plus tool calls), not just the final token. Long-horizon tool use is the hard part — recent recipes focus on agents that call tools correctly over many steps and recover when a step fails. Process supervision is rising — rewarding good intermediate steps, not only the final answer, is becoming the way to fix flawed multi-hop reasoning. The unsolved bits matter — generalisation and reward design are unsettled, and reward hacking is a real failure mode. Treat headline benchmark numbers with care. Most teams should consume, not train — you will get nearly all of this through better off-the-shelf agentic models and good engineering, rather than…
Top comments (0)