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Self-Challenging Language Model Agents
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Proposes Self-Challenging Agent (SCA), where a Llama-3.1-8B agent generates verifiable Code-as-Task (CaT) tasks by exploring the environment and then trains on them with RL or distillation, improving multi-turn tool-use success on M3ToolEval and Tau-Bench.
Problem
Training multi-turn tool-use LLM agents requires many high-quality, feasible, verifiable tasks, but human annotation is costly and automatically generated tasks are often infeasible, ambiguous, or unverifiable.
Approach
An agent plays a challenger role that explores the tool environment and writes tasks in a Code-as-Task format (instruction, verification function, example solution, failure cases), with external code execution filtering out low-quality tasks before the same agent trains as executor via RL or distillation.
Key results
- In self-improvement, Llama-3.1-8B-SCA raises average Pass@1 from 12.0% to 23.5% across four environments, versus 12.9% for PAE.
- In distillation from Llama-3.1-70B, Llama-3.1-8B-SCA reaches 32.2% average Pass@1 versus 30.1% for PAE.
- Human annotation shows the full CaT filter reduces false positives to zero, though false negatives remain at a substantial share.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train with SCA-generated tasks and Llama-3.1-70B demonstration trajectories instead of PAE-generated tasks | Llama-3.1-8B distillation · M³ToolEval and Tau-Bench average | Pass@1: 30.1 -> 32.2 | Helped |
| Train with SCA-generated tasks and Llama-3.1-70B demonstration trajectories instead of PAE-generated tasks | Llama-3.1-8B distillation · M³ToolEval and Tau-Bench average | Pass@4: 52.0 -> 56.8 | Helped |
| Train on self-generated Code-as-Task tasks and own rollout trajectories instead of using PAE | Llama-3.1-8B self-improvement · M³ToolEval and Tau-Bench average | Pass@1: 12.9 -> 23.5 | Helped |
| Train on self-generated Code-as-Task tasks and own rollout trajectories instead of using PAE | Llama-3.1-8B self-improvement · M³ToolEval and Tau-Bench average | Pass@4: 27.7 -> 41.3 | Helped |
| Use only successful teacher trajectories rather than both successful and unsuccessful trajectories (ablation) | Llama-3.1-8B distillation · M³ToolEval and Tau-Bench average | Pass@1: 32.2 -> 27.8 | Hurt |
| Use only successful teacher trajectories rather than both successful and unsuccessful trajectories (ablation) | Llama-3.1-8B distillation · M³ToolEval and Tau-Bench average | Pass@4: 56.8 -> 48.1 | Hurt |
| Train one joint model on aggregate data from all environments rather than a separate model for each environment (ablation) | Llama-3.1-8B-SCA · M³ToolEval and Tau-Bench average | Pass@1: 23.5 -> 23.0 | Hurt |
| Train one joint model on aggregate data from all environments rather than a separate model for each environment (ablation) | Llama-3.1-8B-SCA · M³ToolEval and Tau-Bench average | Pass@4: 41.3 -> 39.8 | Hurt |
| Use online RL (PPO) rather than offline rejection fine-tuning on the same tasks (ablation) | Llama-3.1-8B-SCA self-improvement · M³ToolEval Calculation | Pass@1: 31.8 -> 43.2 | Helped |
| Add an example solution and filter tasks by whether it passes the verification function (ablation) | CaT task generation · Tau-Bench Retail human annotations | False negatives | Helped |
| Add failure cases and filter tasks that pass verification on those cases (ablation) | CaT task generation · Tau-Bench Retail human annotations | False positives | Helped |
Surprising findings
- Distillation using both successful and unsuccessful teacher trajectories outperformed successful-only distillation.
- PAE self-improvement in Airline led to a drop to 0% Pass@4 from the baseline.
Limitations
- Non-trivial false negative rate from ambiguous or incomplete instructions remains even after CaT filtering.
- Gains are environment-specific; joint cross-environment training gave marginal or worse results.
- Suboptimality gap relative to oracle human-curated tasks.
Experimental setup
Datasets: M3ToolEval Tau-Bench
Benchmarks: M3ToolEval Tau-Bench
Metrics: pass@1 pass@4 success rate
Baselines: PAE GPT-4o Llama-3.1-70B Llama-3.1-8B
Builds on: Asymmetric Self-Play PAE REINFORCE PPO GRPO DPO
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