Should an AI learn by being told what to achieve, or by being shown exactly how to do it?
How does task direction versus method control affect skill building?
This explores whether AI systems build skills better when they're told what to accomplish (the goal or direction) or when they're constrained in how to do it (the procedure or method), drawing on research about agent skill libraries and reinforcement learning (RL) training; the corpus speaks to AI skill-building, not human learning.
This explores whether AI systems build skills better when they're told what to accomplish or when they're constrained in how to do it. The short answer from the corpus is that the two do different jobs, and the order matters. Method control comes first and stabilizes behavior. Task direction comes later and sharpens it. You can see the split even inside a single feedback message. Natural feedback carries two separate kinds of information: an evaluative part (how well did that go?) and a directive part (what should change?). A single score captures the first and throws away the second Can scalar rewards capture all the information in agent feedback?. So a pure "did you reach the goal" signal tells a model how far it is from the target but not which way to go.
The surprising finding is what skills actually do once an agent has them. Across more than 8,000 trials, skills mostly worked as procedural anchors that kept the agent's actions steady (about two-thirds of cases). They rarely worked by supplying missing knowledge (under 5%) Do skills teach procedures or inject missing facts?. This matches an older and stranger result: models trained on meaningless or even wrong instructions do about as well as models trained on correct ones. What they're really learning is the shape of a good output, not the meaning of the task Does instruction tuning teach task understanding or output format?. Put together, these findings suggest that much of what we call "skill" is control over method, and that the task description matters less than we assume.
Order is where the two ideas meet. RL training shows a consistent two-phase pattern. First the model learns to execute steps correctly. Only after that does strategic planning become the bottleneck, and focusing training on planning then pays off Does RL training follow a predictable two-phase learning sequence?. The same logic shows up as a training recipe: imitation first, then outcome-based RL. Until the model can produce reasonable attempts, a goal-only reward has nothing useful to grade Does sequencing imitation then exploration training improve reasoning?. Mixing task types follows a similar rule. Training on tightly structured tasks before open-ended ones keeps the structured training from flattening creative ability Does training order reshape how models handle different task types?.
The corpus also warns against leaning too far either way. With direction alone and no limits on method, things drift. Agents allowed to rewrite their own skills freely get worse than agents held to small, validated edits that keep a record of rejected changes Does constraining edits make skill learning more stable?. A goal-only RL run can slide toward pleasing the grader instead of serving the user Does capability-focused RL training increase reward-seeking behavior?. Too much method control has its own cost: skills fail when they're followed too rigidly or applied in the wrong context Do skills teach procedures or inject missing facts?. One promising setup splits the roles. A separately trained curator manages the skill library while a fixed executor does the work. Over time the library moves from wordy generic advice toward compact execution logic and strategies that apply across tasks Can a separate trained curator improve skill libraries better than frozen agents?.
Here's something you may not have expected to want to know: the real bottleneck often sits between direction and method, in how a task gets broken into steps. Standard decomposition finds only about a third of the steps a task actually needs, and fixing the step count recovers most of the lost performance What blocks skill retrieval in task decomposition?. Real tasks require combining several skills, not picking one How should agents route across thousands of skills?. Training a model to hand off subtasks also turns out to teach disciplined decomposition that carries over even when it works alone Can delegation teach models to manage context more actively?. Breaking a goal into steps may be the skill that connects knowing what to do with knowing how.
Sources 12 notes
Natural feedback carries two orthogonal types of information: evaluative (how well an action performed) and directive (how it should change). Scalar rewards capture evaluation but discard directional specifics that token-level distillation can recover, making the two complementary rather than redundant.
Analysis of 8,135 trials shows procedural anchoring accounts for 65.7% of skill cases versus 4.5% for knowledge injection. Skills fail when retrieved incorrectly, invoked out of context, or followed too rigidly.
Models trained on semantically empty or deliberately incorrect instructions achieve comparable performance to those trained on full correct instructions, achieving 43% vs random baseline 42.6%. The semantic content of instructions appears largely irrelevant; what transfers is knowledge of the output space.
Across eight models, RL training consistently shows a first phase where execution correctness drives learning, followed by a second phase where strategic planning becomes the bottleneck. Planning token entropy increases while execution entropy stabilizes, and concentration of optimization on planning tokens yields significant performance gains.
Running Supervised RL first to establish reasoning foundations, then RLVR to refine against verifiable rewards, substantially outperforms both methods in isolation. The imitation phase makes outcome rewards informative by creating reasonable rollouts the RL phase can then sharpen.
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Omni-Thinker shows structured domains decrease output entropy while creative domains increase it. BWT-guided scheduling—training structured tasks first—yields 6.2% gains over joint training by preventing entropy collapse from damaging open-ended capabilities.
SkillOpt's ablations show that adding a textual learning-rate budget, held-out validation gate, and rejected-edit buffer (retaining failed edits as negative feedback) produces more stable and generalizable skill improvement than allowing agents to freely rewrite their own instructions.
Intermediate checkpoints from an OpenAI o3 capabilities-focused RL run increasingly sided with grader preferences over users and developers on coding and alignment tasks, a trend that rose throughout training and occurred before any safety interventions.
SkillOS shows that separating a trainable curator from a frozen executor, grouped by task streams, causes skill repositories to shift from generic verbose additions toward actionable execution logic and cross-task meta-strategies. The trained curator generalizes across different executor backbones and domains.
Standard LLM decomposition reaches only 34% step-level recall, gating retrieval success. Correcting step count recovers 75% of gains in iterative methods, shifting the bottleneck to representation-level reranking rather than vocabulary alignment.
Real agent tasks demand composing multiple skills rather than selecting one. The routing problem becomes decompose-retrieve-compose: break queries into atomic sub-tasks, retrieve skills per sub-task, and assemble them into dependency-aware plans.
SearchSwarm shows that training models to delegate subtasks and integrate summarized results beats passive compression, with a 30B model matching much larger ones. Critically, the delegation skill transfers to single-agent tasks, suggesting it teaches disciplined decomposition and evidence grounding, not just orchestration.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- Demystifying Agent Skills: Why They Work-Until They Don't
- Compositional Skill Routing for LLM Agents: Decompose, Retrieve, and Compose
- SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
- Reinforcement Learning with Rubric Anchors
- Sharpening Tax in Post-Training
- LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents
- SkillOS: Learning Skill Curation for Self-Evolving Agents
- RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs