Stepwise preference

One step, scored, with the correction a researcher would make.

1/9

File metadata

_format:“option-a-stepwise-critique”

_note:“One element per scored step. Element shape is identical to rlhf-data-example.json (traj_id, step_id, category, task, history, current_step, after_step_observation, judge, score, correction) so an existing loader works unchanged. Ships as a JSON object with a `…”

traj_id: afa44e80-42fd-4367-8066-096e63c0e738
step_id: 2
category: research
task: mountaincar-regret-vs-optimal-control
success: null
gt: null
score: 2

history

user

Reinforcement learning has become a powerful paradigm for control and decision-making tasks, yet it faces challenges when applied to real-world problems, including sample efficiency, explainability, interpretability, real-time capability, and training stability. The Mountain Car problem is a canonical low-dimensional continuous control benchmark: an underpowered car in a valley must build momentum through oscillation to reach a hilltop goal against gravity while minimizing propulsion effort. Its dynamics resemble typical physics and engineering systems yet are simple enough to invite analytic treatment. Despite decades of use as a benchmark, the true optimal solution and thus the actual gap to optimality of modern RL agents have not been established. Understanding this gap, and exploring policy representations beyond neural networks for such tasks, motivates the investigation. Research question I'm investigating: How close to optimality are current state-of-the-art RL agents on the Mountain Car benchmark, i.e., what is their regret relative to the optimal control?

current_step

assistant

reasonTwo definitional choices dominate the whole result (which variant, and whether 'optimal' means discounted or undiscounted), plus how much of the interpretable-policy angle to include. Lay out the plan in prose and put the three choices to the researcher before speccing the experiment.

actionask_researcher(questions=[{"header":"Environment","question":"Which Mountain Car variant is the target for the regret study?","options":["Continuous only","Discrete only","Both variants"]},{"header":"Regret basis","question":"How should 'optimal' (and therefore regret) be defined?","options":["Both discounted & undiscounted","Undiscounted true objective","Discounted (agent's objective)"]},{"header":"Interp. scope","question":"How much of the interpretable-policy angle do you want included?","options":["Fit + validate a closed form","Just report the DP policy","Skip for now"]}])

after_step_observation

observation

The agent added: "I'll wait for your choices, then spec out the experiment accordingly."

The researcher picked none of the nine offered options and replied with free text instead:
"We have a deterministic, known dynamics and a 2D state. We could actually compute this without learning at all. We should try computing a solution directly given we that the dynamics are fully known and the state space is small enough to grid?"

judge

The agent's own prose had already reached the right conclusion — "establishing 'optimality' here is an optimal-control problem, not an RL problem", state is 2D, value iteration on a 600x240 grid nails it. Then it stopped and gated a few-CPU-second computation behind a nine-option scoping menu. 1. The menu asks for decisions the computation itself answers better. 'How should optimal be defined' and 'how much of the interpretable-policy angle' are questions about the shape of an object nobody has looked at yet. The session went on to answer both empirically: the policy-surface plot showed the discount bifurcation, and the closed-form derivation made the interpretable-policy question moot by producing a=clip(k*v,+-1) outright. 2. Cost asymmetry. Asking costs a researcher turn and yields a guess; computing costs seconds and yields the ground truth every later choice depends on. 3. The researcher answered with none of the nine options, which is the direct evidence that the menu was not the decision that needed making. Not a wrong plan — a stalled one. The correct next move is the one the agent took immediately after being pushed: write the solver and put up the direct solve. {"Score":2}

correction

error_type:“scoping_menu_instead_of_the_cheap_exact_computation”

first_problematic_step_id:2

critique:“We have a deterministic, known dynamics and a 2D state. We could actually compute this without learning at all. We should try computing a solution directly given we that the dynamics are fully known and the state space is small enough to grid?”

preferred_insertion_point:2

research_principle:“When the ground truth is exactly computable and cheap, compute it before asking anyone to choose the study's scope — the computed object settles most scoping questions better than an a priori answer can.”