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Next big section is on residuals which has devolved to something extremely extremely cursed. They first compare HyperConnections (learnable write, read, mix operators) against a simplied variant where its just a linear combination of various branches and the output is written to a single branch in a roundrobin fashion. They compare both of these with their own variant GatedResidual: 1) RMSNorm each branch 2) Apply projection via a low rank bottleneck 3) Layer is the input coefficients of the different branches 4) Write uses a single branch level scalar

🛠️ Collecting "successful trajectories" alone may not make tool-use agents stronger. Title: Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents URL: A data-synthesis method that builds learnable experience on purpose, rather than harvesting successful runs, for SFT of tool-use agents. Three highlights: 🧩 Highlight 1: Synthesize for dependencies It checks that chains such as lookup, error, retry, confirmation, and update appear repeatedly in the data, and fills the gaps in later synthesis rounds. In the existing candidate pool, only 10.0% of trajectories had a decision after a genuine error. 🎯 Highlight 2: Learn only from grounded actions Actions that rely on information the agent could not see, like IDs known only to the teacher policy, are excluded from training. Failed calls and errors stay in the context but are not imitated. ⚖️ Highlight 3: SFT weighted per trajectory To keep long trajectories from dominating, losses are averaged within each trajectory and then across trajectories. In ablations this gave the largest isolated gain, +8.04 points. 📊 Across five Qwen3 scales, the three-benchmark mean (Tau1, Tau2, BFCL) rose 16.9-29.8 points. A post-trained 4B scored 69.17 on Tau2, versus 28.33 for the 30B-A3B initialization. 💡 It argues for managing the quality of dependencies and grounding, not just data volume. #AIAgents# #LLM#