Pareto frontier optimization
[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.
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Compute the Pareto frontier for multi-objective optimization. Use this skill whenever a task requires finding optimal trade-offs between conflicting metrics (e.g., maximize F1, minimize distance).
SKILL.md
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Pareto Frontier Computation
The Pareto frontier consists of the non-dominated solutions in a multi-objective optimization problem.
Manual Implementation
Here is a simple, robust manual implementation to filter a list of results (e.g. dictionaries) for Pareto optimality:
def get_pareto_frontier(results, maximize_keys, minimize_keys):
"""
results: list of dicts
maximize_keys: list of strings (keys to maximize)
minimize_keys: list of strings (keys to minimize)
"""
pareto_optimal = []
for i, candidate in enumerate(results):
dominated = False
for j, other in enumerate(results):
if i == j: continue
# Check if 'other' dominates 'candidate'
# 'other' dominates if it is at least as good in ALL metrics,
# and strictly better in AT LEAST ONE.
at_least_as_good = True
strictly_better = False
for key in maximize_keys:
if other[key] < candidate[key]:
at_least_as_good = False
break
if other[key] > candidate[key]:
strictly_better = True
if not at_least_as_good:
continue
for key in minimize_keys:
if other[key] > candidate[key]:
at_least_as_good = False
break
if other[key] < candidate[key]:
strictly_better = True
if at_least_as_good and strictly_better:
dominated = True
break
if not dominated:
pareto_optimal.append(candidate)
return pareto_optimal