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Published equation contexts

τ=(s0, (a1,o1), …, (an,on), sn)\tau = \big(s_0,\ (a_1, o_1),\ \dots,\ (a_n, o_n),\ s_n\big)

Why this formula appears here

A language model evaluation is, structurally, a function from a prompt to a string. Grading the string may be very hard, but the object being graded is small, static, and fully observed. An agent produces something categorically larger. The natural unit of observation is a trajectory: τ=(s0, (a1,o1), …, (an,on), sn)\tau = \big(s_0,\ (a_1, o_1),\ \dots,\ (a_n, o_n),\ s_n\big). an initial environment state, an ordered sequence of actions with the observations they returned, and a terminal state. Everything that separates a competent run from a lucky one lives in the middle terms. A conventional success indicator reads only the last one.

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Published contexts (1)

A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.

τ=(s0, (a1,o1), …, (an,on), sn)\tau = \big(s_0,\ (a_1, o_1),\ \dots,\ (a_n, o_n),\ s_n\big)

Equation 1 · Agent Evaluation

Evaluating Trajectories, Not Answers

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

A language model evaluation is, structurally, a function from a prompt to a string. Grading the string may be very hard, but the object being graded is small, static, and fully observed. An agent produces something categorically larger. The natural unit of observation is a trajectory: τ=(s0, (a1,o1), …, (an,on), sn)\tau = \big(s_0,\ (a_1, o_1),\ \dots,\ (a_n, o_n),\ s_n\big). an initial environment state, an ordered sequence of actions with the observations they returned, and a terminal state. Everything that separates a competent run from a lucky one lives in the middle terms. A conventional success indicator reads only the last one.

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